A prospective cohort study of light transmission platelet aggregometry for bleeding disorders: Is testing native platelet-rich plasma non-inferior to testing platelet count adjusted samples?
Bibliographic record
Abstract
Light transmission platelet aggregometry (LTA) is important to diagnose bleeding disorders. Experts recommend testing LTA with native (N) rather than platelet count adjusted (A) platelet-rich plasma (PRP), although it is unclear if this provides non-inferior, or superior, detection of bleeding disorders. Our goal was to determine if LTA with NPRP is non-inferior to LTA with APRP for bleeding disorder assessments. A prospective cohort of patients, referred for bleeding disorder testing, and healthy controls, were evaluated by LTA using common agonists, NPRP and APRP (adjusted to 250 x 10⁹ platelets/l). Recruitment continued until 40 controls and 40 patients with definite bleeding disorders were tested. Maximal aggregation (MA) data were assessed for the detection of abnormalities from bleeding disorders (all causes combined to limit bias), using sample-type specific reference intervals. Areas under receiver-operator curves (AUROC) were evaluated using pre-defined criteria (area differences: < 0.15 for non-inferiority, > 0 for superiority). Forty-four controls and 209 patients were evaluated. Chart reviews for 169 patients indicated 67 had bleeding disorders, 28 from inherited platelet secretion defects. Mean MA differences between NPRP and APRP were small for most agonists (ranges, controls: -3.3 to 5.8; patients: -3.0 to 13.7). With both samples, reduced MA with two or more agonists was associated with a bleeding disorder. AUROC differences between NPRP and APRP were small and indicated that NPRP were non-inferior to APRP for detecting bleeding disorders by LTA, whereas APRP met superiority criteria. Our study validates using either NPRP or APRP for LTA assessments of bleeding disorders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".