Isotretinoin and the Risk of Cardiovascular, Cerebrovascular and Thromboembolic Disorders
Bibliographic record
Abstract
BACKGROUND: Case reports suggest that isotretinoin use is associated with an increased risk of cardiovascular disorders; however, the clinical significance has not been determined. OBJECTIVE: To determine whether isotretinoin increases the risk of cardiovascular outcomes. METHODS: A case crossover study was performed among subjects who filled ≥1 isotretinoin prescription during 1984-2003. Cases were defined as those with a first-ever diagnosis or hospitalization for acute myocardial infarction (MI), stroke, phlebitis/thrombophlebitis, pulmonary embolism (PE) or thrombosis. Exposure to isotretinoin in a 5-month hazard period immediately prior to the index date (calendar date of the diagnosis or hospitalization for the study outcomes) was compared to a 5-month control period. Odds ratios (ORs) along with 95% confidence intervals (CIs) were estimated using conditional logistic regression. RESULTS: Of 30,496 isotretinoin users identified, 872 (3%) cases met the inclusion criteria; 381 (43.7%) had a stroke, 268 (30.7%) phlebitis/thrombophlebitis, 133 (15.3%) MI, 61 (7.0%) PE and 29 (3.3%) thrombosis. When stratified according to type of outcome, ORs were 0.75 (95% CI: 0.38-1.47), 1.31 (95% CI: 0.64-2.69) and 2.00 (95% CI: 0.50-8.00) for stroke, phlebitis/thrombophlebitis and MI, respectively. CONCLUSION: No statistically significant association was found between isotretinoin and cardiovascular, cerebrovascular and thromboembolic outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".