The platelet serotonin‐release assay
Bibliographic record
Abstract
Few laboratory tests are as clinically useful as The platelet serotonin-release assay (SRA): a positive SRA in the appropriate clinical context is virtually diagnostic of heparin-induced thrombocytopenia (HIT), a life- and limb-threatening prothrombotic disorder caused by anti-platelet factor 4 (PF4)/heparin antibodies that activate platelets, thereby triggering serotonin-release. The SRA's performance characteristics include high sensitivity and specificity, although caveats include indeterminate reaction profiles (observed in ∼4% of test sera) and potential for false-positive reactions. As only a subset of anti-PF4/heparin antibodies detectable by enzyme-immunoassay (EIA) are additionally platelet-activating, the SRA has far greater diagnostic specificity than the EIA. However, requiring a positive EIA, either as an initial screening test or as an SRA adjunct, will reduce risk of a false-positive SRA (since a negative EIA in a patient with a "positive" SRA should prompt critical evaluation of the SRA reaction profile). The SRA also provides useful information on whether a HIT serum produces strong platelet activation even in the absence of heparin: such heparin-"independent" platelet activation is a marker of unusually severe HIT, including delayed-onset HIT and severe HIT complicated by consumptive coagulopathy with risk for microvascular thrombosis.
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 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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.003 |
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".