Incidence and causes of heparin-induced skin lesions
Bibliographic record
Abstract
BACKGROUND: Little is known about the incidence and causes of heparin-induced skin lesions. The 2 most commonly reported causes of heparin-induced skin lesions are immune-mediated heparin-induced thrombocytopenia and delayed-type hypersensitivity reactions. METHODS: We prospectively examined consecutive patients who received subcutaneous heparin (most often enoxaparin or nadroparin) for the presence of heparin-induced skin lesions. If such lesions were identified, we performed a skin biopsy, platelet count measurements, and antiplatelet-factor 4 antibody and allergy testing. RESULTS: We enrolled 320 patients. In total, 24 patients (7.5%, 95% confidence interval [CI] 4.7%-10.6%) had heparin-induced skin lesions. Delayed-type hypersensitivity reactions were identified as the cause in all 24 patients. One patient with histopathologic evidence of delayed-type hypersensitivity tested positive for antiplatelet-factor 4 antibodies. We identified the following risk factors for heparin-induced skin lesions: a body mass index greater than 25 (odds ratio [OR] 4.6, 95% CI 1.7-15.3), duration of heparin therapy longer than 9 days (OR 5.9, 95% CI 1.9-26.3) and female sex (OR 3.0, 95% CI 1.1-8.8). INTERPRETATION: Heparin-induced skin lesions are relatively common, have identifiable risk factors and are commonly caused by a delayed-type hypersensitivity reaction (type IV allergic response).
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".