Evaluation of a diagnostic platelet aggregation test strategy for platelet rich plasma samples with low platelet counts
Bibliographic record
Abstract
Abstract Introduction Light transmission aggregometry (LTA) is important for diagnosing platelet function disorders (PFD) and von Willebrand disease (VWD) affecting ristocetin‐induced platelet aggregation (RIPA). Nonetheless, data is lacking on the utility of LTA for investigating thrombocytopenic patients and platelet rich plasma samples with low platelet counts (L‐PRP). Previously, we developed a strategy for diagnostic LTA assessment of L‐PRP that included: (1) acceptance of referrals/samples, regardless of thrombocytopenia severity, (2) tailored agonist selection, based on which are informative for L‐PRP with mildly or severely low platelet counts, and (3) interpretation of maximal aggregation (MA) using regression‐derived 95% confidence intervals, determined for diluted control L‐PRP (C‐L‐PRP). Methods To further evaluate the L‐PRP LTA strategy, we evaluated findings for a subsequent patient cohort. Results Between 2008 and 2021, the L‐PRP strategy was applied to 211 samples (11.7% of all LTA samples) from 192 unique patients, whose platelet counts (median [range] × 109/L) for blood and L‐PRP were: 105 [13–282; 89% with thrombocytopenia] and 164 [17–249], respectively. Patient‐L‐PRP had more abnormal MA findings than simultaneously tested C‐L‐PRP (p‐values <0.001). Among patients with accessible electronic medical records (n = 181), L‐PRP LTA uncovered significant aggregation abnormalities in 45 (24.9%), including 18/30 (60%) with <80 × 109 platelets/L L‐PRP, and ruled out PFD, and VWD affecting RIPA, in others. The L‐PRP LTA strategy helped diagnose VWD affecting RIPA, Bernard Soulier syndrome, familial platelet disorder with myeloid malignancy, suspected ITGA2B/ITGB3‐related thrombocytopenia, and acquired PFD. Conclusion Diagnostic LTA with L‐PRP, using a strategy that considers thrombocytopenia severity, is feasible and informative.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".