2026

Revised: September 1, 2026

A New Way of Looking Inside Your Heart Without Surgery – The Best Way to Predict a Heart Attack

How to Use This Article

Medical disclaimer: This article is for education only and is not medical advice. Always consult your clinician for personal guidance.

Easy Read

What a Heart Scan Can — and Can’t — Tell You About Your Risk

The problem with guessing

For decades, doctors have estimated heart attack risk with a calculator. You plug in age, sex, blood pressure, cholesterol, whether you smoke, whether you have diabetes. Out comes a percentage.

These calculators work reasonably well for estimating risk across groups of people, but their precision for any particular individual is limited. Two people with identical numbers can have completely different arteries — one clean, one full of disease. The calculator can’t tell them apart, because it never looks at the artery.

A calcium score gets closer. It’s a quick CT scan that measures calcium deposits in the walls of the heart’s arteries. Calcium is a signal that disease is present. But calcium is only part of the picture. Atherosclerotic plaque — buildup inside the artery wall that can contain cholesterol-rich material, fibrous tissue, and calcium — can be present even when little or no calcium shows up on the scan, especially in younger people.

Looking directly

Coronary CT angiography, or CCTA, goes further. You get an IV of contrast dye, lie in a scanner for a few seconds, and the machine builds a detailed picture of the arteries themselves: where plaque sits, how much there is, and what it’s made of. No catheters. No incisions. No hospital stay.

The obvious question is whether that extra detail actually helps predict who has a heart attack. In late 2025, a large Swedish study gave the clearest answer yet.

The Swedish study

Researchers scanned 24,791 people aged 50 to 64 who had no known heart disease, then followed them for about eight years. During that time, 304 had a heart attack or died of coronary heart disease.

What predicted those events best wasn’t a single tight narrowing. It was how widespread the disease was. The heart’s arteries are mapped into 18 segments, and researchers counted how many contained any plaque at all. People with plaque in more than four segments had roughly five times the risk of an event compared with people who had plaque in just one or two.

Here’s the part that surprised people. Once you accounted for how widespread the plaque was, having a major blockage — a narrowing of 50% or more — no longer independently predicted a first heart attack in this group. The total amount and spread of disease mattered more than the tightest single spot.

Adding CCTA to the standard calculator and calcium score did improve prediction, but modestly. That was the researchers’ own word: modestly. In practical terms, among people who went on to have an event, about 14 in 100 were correctly moved into a higher-risk category. Among people who didn’t have an event, about 2 in 100 were wrongly moved up. That’s a real improvement. It isn’t a revolution.

Where AI comes in — and the trap

Software can now analyze these scans automatically, measuring plaque volume and composition throughout the whole heart instead of relying on a radiologist’s eye.

You’ll see a striking number quoted about this: an AUC of 0.91, compared with about 0.77 for expert human readers. AUC is a measure of how well a test separates people who have a condition from people who don’t. It is not the same thing as 91% accuracy, and that difference matters here.

It matters because there are two separate questions:

Does this scan correctly identify narrowing that exists right now? This gets checked against invasive angiography, the gold standard.
Does this scan predict who will have a heart attack in the future?

The 0.91 answers question one. It means the software matched the gold standard on present-day anatomy more closely than human readers did. That’s genuinely valuable — it makes readings more consistent from one radiologist to the next.

It does not mean the software predicts heart attacks that well. In the prognostic studies discussed here, event-prediction scores are closer to roughly 0.75 to 0.81 — not 0.91. In one contemporary registry, adding quantitative plaque analysis did improve prediction compared with calcium scoring alone.

Anyone quoting the diagnostic number as if it were the prediction number is overselling the technology.

What plaque composition tells us

Not all plaque behaves the same way. Plaque containing more low-attenuation material — an imaging feature associated with lipid-rich, higher-risk plaque characteristics — has been linked to greater risk than densely calcified plaque. In one large trial, low-attenuation plaque was the single strongest predictor of a future heart attack.

Be careful with the popular shorthand, though. Calcium isn’t harmless, and low-attenuation plaque isn’t automatically dangerous — it’s a mixed category, and only some of it has the unstable structure that ruptures. The honest version is simpler: what plaque is made of adds information that a calcium score alone can’t give you.

Why this matters for women

One of the most useful findings in this research involves differences between men and women. Women generally have less plaque than men do. But in one large registry, each increment of plaque was associated with more added risk in women.

The same increase in plaque volume was associated with about a 17.7% rise in event risk in women, versus 5.3% in men. For low-attenuation plaque specifically, it was 27.1% versus 11.6%.

That suggests the same absolute amount of plaque may not carry exactly the same prognostic meaning in women and men — an important consideration as quantitative plaque measurement becomes more widely used. It does not yet mean there is an established, separate treatment threshold for women.

Can plaque be changed?

Yes, at least the plaque itself. A randomized trial of 80 patients already taking statins used repeat scans over 18 months. Low-attenuation plaque decreased in patients given icosapent ethyl, a prescription omega-3 medication, while it increased in the placebo group.

That’s meaningful mechanistic evidence from a small study. It is not proof that scanning people saves lives.

The question nobody has answered yet

Here’s the honest bottom line.

We know CCTA finds disease that calculators and calcium scores don’t fully capture. We know AI analysis makes reading these scans more consistent. We know doctors change their treatment plans when they see the results — in one study, management changed for more than half of patients.

What we don’t yet know is whether scanning people and treating what turns up actually prevents more heart attacks than current care does. No completed randomized trial has shown that. A large one called TRANSFORM is running now and is designed to test exactly this question.

Until it reports, the fair summary is this: seeing your arteries directly gives you and your doctor real information that risk factors alone can’t provide. Whether acting on that information changes your odds is still being worked out — carefully, by researchers who will publish the answer either way.

Deep Dive

A New Way of Looking Inside Your Heart Without Surgery — Can Advanced CCTA Improve Heart-Attack Risk Prediction?

Traditional Risk Models, Coronary CT Angiography, and AI-Driven Quantitative Plaque Analysis: What the Evidence Does and Does Not Show

The SCAPIS Analysis and the Shift Toward Disease-Based Risk Assessment

The Bergström et al. analysis of the Swedish CArdioPulmonary bioImage Study (SCAPIS), published online in the Journal of the American Medical Association on November 9, 2025 and in print as JAMA. 2026;335(3):245–254, is the largest population-based test to date of a simple question: does looking directly at the coronary arteries tell you something that risk factors and a calcium score do not?¹

The study followed 24,791 individuals aged 50 to 64 years, randomly recruited from the general population at six Swedish university hospitals and free of established cardiovascular disease at baseline, for a median of 7.8 years.¹ The outcome was a first nonfatal myocardial infarctionSee Heart Attack for the full entry. or death from coronary heart disease; 304 such events occurred. The question was whether adding coronary computed tomography angiography (CCTA) findings to a model containing the Pooled Cohort Equation (PCE) and the coronary artery calcium score (CACS) improved prediction.¹

The extent of atherosclerosis mattered more than any single stenosis. The Segment Involvement Score (SIS), which counts how many of the 18 coronary segments contain any plaque, tracked strongly with events: an SIS of 3 to 4 carried a hazard ratio (HR) of 2.71 (95% CI, 1.34–5.44) and an SIS greater than 4 an HR of 5.27 (95% CI, 2.50–11.07), relative to lower scores. The presence of noncalcified atherosclerosis carried an HR of 1.66 (95% CI, 1.23–2.22).¹

Predictive variable	Hazard ratio	95% CI	Model
SIS 1–2	1.00	Reference	Fully adjusted
SIS 3–4	2.71	1.34–5.44	Fully adjusted
SIS >4	5.27	2.50–11.07	Fully adjusted
Noncalcified atherosclerosis	1.66	1.23–2.22	Fully adjusted
Stenosis ≥50%	1.22	0.89–1.69	Fully adjusted

Table 1. Association of CCTA findings with first coronary events in SCAPIS.¹ All values are from the fully adjusted model. Note that once plaque extent is accounted for, obstructive stenosis (≥50%) is not independently associated with first events in this primary-prevention cohort — the confidence interval crosses 1.0. Unadjusted and partially adjusted models give larger stenosis estimates and should not be quoted as if they were adjusted results.

Adding the CCTA variables to a model containing PCE and CACS improved discrimination modestly: the C-statistic rose from 0.764 to 0.779 (P = .004).¹ Reclassification improved as well, with a net reclassification improvement of 0.133 (95% CI, 0.031–0.165). Among participants who went on to have an event, 14.2% were correctly moved into a higher risk category; among those who did not have an event, 1.6% were incorrectly moved upward.¹ Because the overall event rate was low, most of this reclassification occurred among people the PCE had labeled low risk (<5%) — which is precisely the group in which a missed diagnosis is most consequential, and also the group in which unnecessary treatment is hardest to justify.

The investigators themselves characterize the gain as modest.¹ That word is worth keeping. SCAPIS shows that direct imaging of plaque adds real, statistically robust information beyond risk factors and calcium scoring in a middle-aged primary-prevention population. It does not show that imaging transforms individual risk prediction, and it was not designed to show that acting on the imaging improves outcomes.

Two Different Questions: Detecting Stenosis vs Predicting Events

Much of the confusion in the popular coverage of AI-assisted CCTA comes from treating two very different performance metrics as interchangeable. They are not, and conflating them inflates the apparent capability of the technology.

Diagnostic accuracy asks: how well does the test agree with a reference standard — typically invasive quantitative coronary angiography (QCA) — about whether a stenosis is present right now?
Prognostic discrimination asks: how well does the test rank people by their likelihood of having a cardiovascular event in the future?

An area under the curve (AUC) of 0.91 for the first question is not evidence of an AUC of 0.91 for the second. Diagnostic AUCs against an anatomic reference standard are systematically higher than event-prediction AUCs, because future events depend on plaque biology, hemodynamics, thrombotic propensity, treatment, and chance — not only on anatomy. The two tables below are therefore presented separately and should not be merged into a single ranking.

Diagnostic accuracy: agreement with invasive angiography

In a post hoc analysis of the PACIFIC-1 cohort (208 patients with new-onset stable chest pain, all of whom underwent both CCTA and invasive QCA), AI-guided quantitative CT (AI-QCT) was compared directly with human readers of differing experience for the detection of ≥50% stenosis on a per-patient basis.³

Reader	AUC (95% CI)	Task
AI-QCT	0.91 (0.87–0.95)	≥50% stenosis vs QCA
Level-3 expert reader	0.77 (0.70–0.83)	≥50% stenosis vs QCA
Level-2 reader A	0.79	≥50% stenosis vs QCA
Level-2 reader B	0.76	≥50% stenosis vs QCA

Table 2. Per-patient diagnostic accuracy for obstructive stenosis, PACIFIC-1 post hoc analysis, invasive QCA as reference standard.³ These are diagnostic AUCs. They describe agreement with invasive angiography about present anatomy. They do not describe prediction of myocardial infarction.

The correct reading of this result is that automated quantitative analysis agreed with the invasive reference standard more closely than expert visual assessment did, and did so reproducibly. That is a meaningful finding about interpretive consistency — visual CCTA reading is known to be experience-dependent and to overestimate stenosis — but it is a statement about diagnosis, not about prognosis.

Prognostic discrimination: predicting future events

In the CONFIRM2 registry, AI-QCT was tested for incremental prognostic value over the qualitative and semiquantitative measures currently recommended in practice. Adding AI-QCT improved discrimination for major adverse cardiovascular events over CAD-RADS 2.0, CACS, and the modified Duke Index.⁴

Comparator (alone)	AUC alone	AUC + AI-QCT
CAD-RADS 2.0	0.79	0.81
Coronary artery calcium score	0.70	0.79
Modified Duke Index	0.76	0.81
PCE + CACS (SCAPIS, + CCTA)¹	0.764	0.779

Table 3. Prognostic discrimination for future events.¹˒⁴ The CONFIRM2 rows describe specific models within one registry of symptomatic patients referred for CCTA; the SCAPIS row describes an asymptomatic general-population cohort and non-AI CCTA variables. These are not interchangeable, and none of them is a universal performance characteristic of AI-QCT.

Taken together, the honest summary is this: quantitative, AI-assisted CCTA measurements can provide incremental prognostic information beyond conventional clinical risk assessment, calcium scoring, and qualitative CCTA reads — but the magnitude of that improvement varies substantially with the population studied, the endpoint chosen, and the model specification. In the contemporary prognostic studies discussed here, reported event-prediction AUCs for AI-QCT are approximately 0.75 to 0.81 rather than 0.90.¹˒⁴

What Quantitative Plaque Analysis Measures, and Why It Matters

Traditional risk equations such as the Framingham Risk Score and the Pooled Cohort Equations infer vascular risk from surrogate variables — age, sex, blood pressure, lipids, smoking, diabetes — rather than from the artery itself.⁹˒¹⁰ They perform reasonably at the population level and imperfectly at the individual level, because the relationship between risk factors and plaque development is heterogeneous. Two people with identical risk-factor profiles can have very different coronary arteries.

Quantitative CCTA measures the disease directly. Rather than reporting only the tightest narrowing, it characterizes plaque throughout the coronary tree by volume, composition, and vessel remodeling. Several of these features carry prognostic information:

Total plaque volume (TPV). Higher total atherosclerotic burden is independently associated with future events across multiple cohorts. Effect sizes are study-specific and depend on the population, the units in which volume is expressed, and the covariates adjusted for; a risk estimate from one cohort should not be quoted as a general property of plaque volume.⁴˒⁵
Noncalcified plaque (NCP) volume. In CONFIRM2, each 50-mm³ increase in noncalcified plaque was associated with a 1% relative increase in MACE risk in women and 11.6% in men.⁵
Low-attenuation plaque (≤30 HU). A CT marker associated with lipid-rich, necrotic-core plaque characteristics — an imaging correlate rather than a direct measurement of necrotic core. In SCOT-HEART, low-attenuation plaque burden was the strongest predictor of fatal or nonfatal myocardial infarction (adjusted HR 60 per doubling; 95% CI, 1.10–2.34), and a burden above 4% was associated with an HR of 4.65 (95% CI, 2.06–10.5).²
Positive (outward) remodeling. Outward expansion of the vessel wall may allow substantial plaque to accumulate before marked luminal narrowing becomes apparent, which is one reason lumen-focused assessment can underestimate disease burden.

The biology underlying these measurements is more nuanced than a simple “calcium is safe, soft plaque is dangerous” dichotomy. Dense calcification is generally associated with more stable lesion behavior, and calcification does often follow inflammatory injury as part of a healing response — but calcified plaque is not inert, and calcium burden remains a strong marker of overall atherosclerotic disease. Conversely, noncalcified plaque is not synonymous with vulnerable plaque; it is a heterogeneous category, only part of which has the lipid-rich, thin-capped phenotype associated with rupture. The useful statement is narrower and better supported: plaque composition adds prognostic information that calcium score alone does not capture.

A related point is frequently overstated in consumer coverage. It is well documented that many acute coronary events arise from lesions that were not severely obstructive on prior imaging, which is a central reason plaque burden and composition can carry information beyond stenosis severity. It does not follow that “most heart attacks are caused by non-obstructive soft plaque” — that phrasing compresses several distinct issues (pre-event stenosis severity, plaque phenotype, rupture versus erosion, and the limitations of retrospective angiographic comparison) into a single claim that the evidence does not cleanly support.

Scanner Technology: What Wide-Detector CT Actually Delivers

Contemporary wide-detector CT systems can acquire the entire heart within a single cardiac cycle in appropriately selected patients. This reduces misregistration (“stitching”) artifacts that arise when a volume is assembled from multiple heartbeats, and it permits high-quality coronary imaging at relatively low radiation doses when protocols are optimized.

Several caveats belong alongside that statement, because they are routinely omitted:

Marketing terms such as “640-slice” describe a reconstruction characteristic of a wide-area detector system rather than an independent measure of image quality, and they do not by themselves establish superior outcome prediction. Detector coverage (for example, 16 cm) and acquisition architecture are the more meaningful descriptors.
Radiation dose cannot be stated as a single number. It varies with scanner generation, prospective versus retrospective gating, tube voltage and current, heart rate and rhythm, body habitus, scan length, and reconstruction algorithm.¹¹ Sub-millisievert coronary CTA is achievable in favorable patients with optimized protocols; it is not a general expectation.
Spatial resolution figures are scanner- and protocol-specific and should be verified against the manufacturer’s technical specification for the exact system and acquisition mode in question before being cited.
Single-beat acquisition reduces — but does not eliminate — the need for heart-rate control, and does not eliminate motion artifact.

The practical significance of wide-detector systems for this discussion is that reproducible, lower-dose acquisition makes serial imaging more feasible, which in turn makes longitudinal plaque tracking a technically feasible clinical and research proposition. Routine serial CCTA performed specifically to monitor plaque remains an evolving strategy rather than established standard care for most patients.

Is Plaque Modifiable? EVAPORATE and the Limits of What It Shows

EVAPORATE, a randomized, double-blind, placebo-controlled trial of 80 patients with elevated triglycerides on statin therapy, used serial CCTA to assess the effect of icosapent ethyl on plaque progression over 18 months. It found reduction in low-attenuation plaque volume in the treatment arm alongside progression in the placebo arm.⁷

EVAPORATE is a mechanistic imaging trial. It was not an AI-QCT study and it was not powered for clinical events. It supports the proposition that plaque composition is modifiable and measurable over time; it does not validate the prognostic performance of any particular AI analysis platform, and it should not be cited as though it did.

TRANSFORM: A Trial Designed to Test the Outcome Question

TRANSFORM (NCT06112418) is a prospective, randomized, open-label, blinded-endpoint trial enrolling patients at elevated cardiovascular risk without known symptomatic disease. Participants are randomized to guideline-directed risk-factor-based care or to a care strategy driven by an AI-based coronary plaque staging system, with repeat imaging at 24 months in the imaging arm.⁸

TRANSFORM is ongoing. Its results are not yet available, and it therefore cannot be cited as evidence supporting any conclusion about the effectiveness of an imaging-guided prevention strategy. Its importance lies in what it is designed to establish: whether identifying and staging plaque earlier, and treating accordingly, actually reduces cardiovascular events compared with current practice. That is the question the existing observational and diagnostic literature cannot answer.

Impact on Clinical Decision-Making

There is reasonable evidence that quantitative plaque analysis changes what clinicians do. In the CERTAIN study, a multicenter crossover study of 750 consecutive patients referred for CCTA at five expert sites, physicians recorded their diagnosis and management plan based on conventional site interpretation and then repeated the assessment after AI-QCT analysis.⁶

Diagnosis or management changed in 1% of patients (P < .001).⁶
Statin initiation or intensification increased in an additional 1% of patients, and aspirin initiation in an additional 23.0% (P < .001 for both).⁶
The anticipated need for downstream noninvasive and invasive testing fell by 1% (P < .001).⁶

These are changes in physician intent measured within a study design in which the same physicians assessed the same patients twice, in a fixed order, at high-volume expert centers. That design cannot exclude ordering effects, and intent to prescribe is not the same as improved outcomes. The finding is genuinely encouraging about clinical utility and genuinely insufficient as evidence of clinical benefit.

Coverage for AI-based coronary plaque analysis has expanded among United States payers since 2024, but policies remain plan-, indication-, and region-specific. Patients and clinicians should verify coverage directly with the payer rather than relying on general statements of availability.

Sex-Specific Implications

One of the most clinically relevant findings in this literature concerns sex differences in how plaque burden translates into risk. In the CONFIRM2 registry (3,551 symptomatic patients, 49.5% women, mean follow-up 4.8 ± 2.2 years), women had roughly half the plaque burden of men and about half the MACE rate (3.2% vs 6.1%). But the relative risk conferred by each increment of plaque was consistently higher in women.⁵

Plaque measure (per 50 mm³ increase)	Increase in MACE risk, women	Increase in MACE risk, men
Total plaque volume	+17.7%	+5.3%
Noncalcified plaque	+27.1%	+11.6%
Calcified plaque	+22.9%	+5.4%

Table 4. Sex-specific relative risk per 50-mm³ increment, CONFIRM2 registry (P for interaction < .001).⁵ Note that total plaque volume, noncalcified plaque, and calcified plaque have distinct values and must not be quoted interchangeably.

The clinical implication is that a plaque volume that looks reassuringly “low” by absolute standards derived largely from men may carry meaningful prognostic weight in a woman. Quantitative measurement, which reports actual volumes rather than a qualitative impression, is well suited to detecting exactly this pattern — provided the interpretation is sex-aware.

Synthesis and Conclusion

The central argument holds. Coronary CT angiography identifies atherosclerotic disease that risk-factor equations and calcium scoring do not fully characterize, and quantitative, AI-assisted analysis of those images adds diagnostic reproducibility and incremental prognostic information. SCAPIS provides strong contemporary evidence that CCTA improves prediction of first coronary events beyond the PCE and CACS in a middle-aged general population, particularly among people those tools classify as low risk.¹ CONFIRM2 shows that quantitative plaque measurement improves discrimination over the qualitative and semiquantitative measures used in practice today, and that plaque burden carries greater relative risk in women.⁴˒⁵ CERTAIN shows that clinicians change their management when given this information.⁶

What the evidence does not yet establish is equally important:

It does not establish that AI-assisted CCTA predicts cardiovascular events with an AUC near 0.90. That figure comes from diagnostic accuracy against invasive angiography, not from event prediction.³˒⁴
It does not establish that imaging-guided prevention improves clinical outcomes. No completed randomized trial has demonstrated this. TRANSFORM is designed to test it and has not reported.⁸
It does not establish that any single scanner configuration or analysis platform is superior in outcome terms.

The reasonable conclusion is a strong one without being an overclaim: direct visualization and quantification of coronary plaque represents a substantive advance over inference from risk factors alone, and it identifies disease in people whom conventional tools reassure. Whether acting on that information reduces heart attacks is a question currently under randomized investigation, and the answer will come from TRANSFORM and trials like it rather than from registries, diagnostic accuracy studies, or extrapolation.

For an individual patient, the practical takeaway is unchanged by any of these caveats: finding plaque may identify an opportunity to intensify evidence-based preventive treatment, and serial CCTA studies demonstrate that plaque characteristics are measurably modifiable.⁷ The appropriate posture toward the technology is confident engagement paired with accurate expectations about what the evidence currently supports.

References

Transparency Note: This blog post was created with assistance from AI tools. The final content has been carefully reviewed and edited by the author, who is responsible for its accuracy. The information provided is for educational purposes only and does not constitute medical advice.

CuringHeartDisease

Always searching for a cure
Peter Megdal, PhD

A New Way of Looking Inside Your Heart Without Surgery – The Best Way to Predict a Heart Attack

How to Use This Article

Medical disclaimer: This article is for education only and is not medical advice. Always consult your clinician for personal guidance.

Easy Read

What a Heart Scan Can — and Can’t — Tell You About Your Risk

The problem with guessing

Looking directly

The Swedish study

Where AI comes in — and the trap

What plaque composition tells us

Why this matters for women

Can plaque be changed?

The question nobody has answered yet

Deep Dive

A New Way of Looking Inside Your Heart Without Surgery — Can Advanced CCTA Improve Heart-Attack Risk Prediction?

The SCAPIS Analysis and the Shift Toward Disease-Based Risk Assessment

Two Different Questions: Detecting Stenosis vs Predicting Events

Diagnostic accuracy: agreement with invasive angiography

Prognostic discrimination: predicting future events

What Quantitative Plaque Analysis Measures, and Why It Matters

Scanner Technology: What Wide-Detector CT Actually Delivers

Is Plaque Modifiable? EVAPORATE and the Limits of What It Shows

TRANSFORM: A Trial Designed to Test the Outcome Question

Impact on Clinical Decision-Making

Sex-Specific Implications

Synthesis and Conclusion

References

Share This Post

Related Articles

RECENT ARTICLES

A New Way of Looking Inside Your Heart Without Surgery – The Best Way to Predict a Heart Attack

How to Use This Article

Medical disclaimer: This article is for education only and is not medical advice. Always consult your clinician for personal guidance.

Easy Read

What a Heart Scan Can — and Can’t — Tell You About Your Risk

The problem with guessing

Looking directly

The Swedish study

Where AI comes in — and the trap

What plaque composition tells us

Why this matters for women

Can plaque be changed?

The question nobody has answered yet

Deep Dive

A New Way of Looking Inside Your Heart Without Surgery — Can Advanced CCTA Improve Heart-Attack Risk Prediction?

The SCAPIS Analysis and the Shift Toward Disease-Based Risk Assessment

Two Different Questions: Detecting Stenosis vs Predicting Events

Diagnostic accuracy: agreement with invasive angiography

Prognostic discrimination: predicting future events

What Quantitative Plaque Analysis Measures, and Why It Matters

Scanner Technology: What Wide-Detector CT Actually Delivers

Is Plaque Modifiable? EVAPORATE and the Limits of What It Shows

TRANSFORM: A Trial Designed to Test the Outcome Question

Impact on Clinical Decision-Making

Sex-Specific Implications

Synthesis and Conclusion

References

Share This Post

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RECENT ARTICLES

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