Impact of the patient population on the risk for heparin-induced thrombocytopenia
Bibliographic record
Abstract
The frequency of immune heparin-induced thrombocytopenia (HIT) varies among prospective studies. It is unknown whether this is caused by differences in the heparin preparations, the patient populations, or the types of serologic assay used to confirm the diagnosis. Seven hundred forty-four patients were studied from 3 different clinical treatment settings, as follows: unfractionated heparin (UFH) during or after cardiac surgery (n = 100), UFH after orthopedic surgery (n = 205), and low-molecular-weight heparin (LMWH) after orthopedic surgery (n = 439). Both an activation assay and an antigen assay were used to detect heparin-dependent IgG (HIT-IgG) antibodies. By activation assay, the frequency of HIT-IgG formation ranged from a low of 3.2% in orthopedic patients receiving LMWH to a high of 20% in cardiac patients receiving UFH; by antigen assay, the corresponding frequencies ranged from 7.5% to 50%. Both UFH use (P =.002) and cardiac surgery (P =.01) were more likely to be associated with HIT-IgG formation. However, among patients in whom HIT-IgG formed and who were administered UFH, the probability for HIT was higher among orthopedic patients than among cardiac patients (by activation assay: 52.6% compared with 5%; odds ratio, 21.1 [95% CI, 2.2-962.8]; P =.001; by antigen assay: 34.5% compared with 2.0%; odds ratio, 25.8 [95% CI, 3.2-1141]; P <.001). It is concluded that there is an unexpected dissociation between the frequency of HIT-IgG formation and the risk for HIT that is dependent on the patient population. HIT-IgG antibodies are more likely to form in patients who undergo cardiac surgery than in orthopedic patients, but among patients in whom antibodies do form, orthopedic patients are more likely to develop HIT. (Blood. 2000;96:1703-1708)
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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 | 0.003 | 0.018 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".