Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is a clinical-pathological disorder; thus, laboratory testing for the pathogenic platelet-activating antiplatelet factor 4 (PF4)/heparin antibodies is central for diagnosis. The "iceberg" model summarizes the inter-relationship between platelet activation assays and PF4-dependent immunoassays, with platelet-activating antibodies comprising a subset of anti-PF4/heparin antibodies. The platelet serotonin-release assay (SRA), performed by reference laboratories, has high sensitivity and specificity for HIT (~95% each), and is especially suited for detecting highly pathogenic HIT sera containing both heparin-dependent and heparin-independent platelet-activating antibodies; this latter subgroup of antibodies explains "autoimmune HIT" disorders (delayed-onset, persisting, spontaneous, heparin "flush," fondaparinux-associated). Recently, SRA-negative HIT has become recognized, in which serum from some HIT patients contains subthreshold levels of platelet-activating antibodies (by SRA) that become detectable using a PF4-enhanced platelet activation assay. Unusual immunologic features of HIT include early antibody detectability (at onset of platelet count fall) and antibody transience (seroreversion). Widely available PF4-dependent enzyme immunoassays (EIAs) have high sensitivity but poor specificity for HIT, although specificity is enhanced with IgG-specific EIAs and strong positive results; unfortunately, EIA results are usually not available in real time. Automated rapid immunoassays, such as the chemiluminescence immunoassay (CLIA) and latex immunoturbidimetric assay (LIA), facilitate real-time laboratory diagnosis. Recently available likelihood ratio (LR) data for positive (LR+) and negative (LR-) test results allow clinicians to adjust their pretest probabilities for HIT, using Bayesian analysis, into real-time posttest probabilities that are dramatically increased (test positive) or decreased (test negative). Moreover, (semi-)quantitative CLIA- and LIA-positive results (weak, moderate, strong positive) can further refine the posttest probability of HIT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".