Combination of 4Ts score and PF4/H-PaGIA for diagnosis and management of heparin-induced thrombocytopenia: prospective cohort study
Bibliographic record
Abstract
Rapid exclusion of heparin-induced thrombocytopenia (HIT) is needed to determine which patients can continue to receive heparin. In this prospective management study, 526 participants had a 4Ts score, rapid particle gel immunoassay (platelet factor 4/heparin [PF4/H]-PaGIA), and serotonin-release assay (SRA) performed. While awaiting SRA results, participants with a low 4Ts score (irrespective of PF4/H-PaGIA result) or intermediate 4Ts score plus a negative PF4/H-PaGIA result received prophylactic doses of danaparoid or fondaparinux; all others received therapeutic doses of nonheparin anticoagulants. The primary outcome was the frequency of management failures defined as HIT-positive participants with a low 4Ts score (irrespective of PF4/H-PaGIA result) or intermediate 4Ts score plus negative PF4/H-PaGIA result. Six participants (1.1%; 95% confidence interval [CI], 0.2-2.1%) were management failures. A negative PF4/H-PaGIA result reduced the pretest probability of HIT from 1.9% to 0% (95% CI, 0-1.3%), 6.7% to 0% (95% CI, 0-2.7%), and 36.6% to 0% (95% CI, 0-14.3%) in the low, intermediate, and high score groups, respectively. A positive PF4/H-PaGIA result increased the probability of HIT in the low score group to 15.4% (95% CI, 5.9-30.5). Thus, a low or intermediate 4Ts score plus negative PaGIA result excluded HIT, whereas any other combination of results justified the use of alternative anticoagulants until HIT could be excluded. This trial was registered at www.clinicaltrials.gov as #NCT00489437.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".