Cardiovascular risk in patients with alopecia areata:a cross-sectional study
Bibliographic record
Abstract
Introduction: Numerous studies indicate that alopecia areata is associated with systemic inflammation with an increased serum level of proinflammatory cytokines.Recent studies have indicated a higher incidence of cardiovascular diseases in patients with alopecia areata.Objective: To evaluate the cardiovascular risk calculated with various scoring systems in patients with alopecia areata compared to the controls.Material and methods: In the present study, the cardiovascular risk was evaluated in 91 patients with alopecia areata and 47 controls with the use of QRESEARCH risk estimator version 3 (QRISK-3), Framingham Risk score -Coronary heart disease (FRS-CHD), Framingham Risk Score -Cardiovascular disease (FRS-CVD), Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator Plus and Systematic Coronary Risk Evaluation (SCORE) risk charts.Results: An increased median QRISK-3 was observed in patients with alopecia areata compared to controls (1.2 [0.5-5.5] vs. 0.9 [0.2-3.2];p < 0.05).Moreover, higher median FRS-CHD, FRS-CVD, ASCVD Risk Estimator Plus scores were detected in patients with alopecia areata compared to control subjects (4.5 (2.4-9) vs. 3.35 (1.5-6.7);0.85 (0.1-2.4) vs. 0.45 (0.1-1) and 2.5 (0.7-8.2) vs. 1.9 (0.8-4.3), respectively).However, those differences were not statistically significant (p > 0.05).In patients with alopecia areata, a positive correlation was observed between QRISK-3 and the age of the patient, body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol, LDL-cholesterol and triglycerides.Conclusions: Alopecia areata is associated with an increased cardiovascular risk.Regular cardiovascular screening should be recommended to every patient with alopecia areata.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".