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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".