How Trustworthy is Light Transmittance Platelet Aggregometry With Low Platelet Count Samples? Insights From Test Replicates and Retrospective Analysis of Several Decades of Diagnostic Samples
Bibliographic record
Abstract
ABSTRACT Introduction Light transmission platelet aggregometry (LTA) is useful to diagnose platelet function disorders (PFD). We evaluated the precision and reproducibility of LTA with low platelet count platelet‐rich plasma (LPRP). Methods LPRP maximal aggregation (MA) precision for informative agonists and LTA reproducibility were assessed using multiple replicates for adjusted control LPRP (CLPRP) and retrospective analysis of several decades of consecutive patient LPRP (PLPRP) and CLPRP tests with replicates between and/or within tests. Results Intra‐subject CLPRP had acceptable MA CVs and tracings unless platelets were ≤ 25 × 10 9 /L. Among tests with replicates, PLPRP with ≤ 25 × 10 9 platelets/L were uncommon (6/247 samples) and 5/6 showed pathognomonic Bernard–Soulier syndrome (BSS) findings. Among evaluated patients ( n = 195; diverse diagnoses), 44.6% had abnormal LTA findings on ≥ 1 tests after excluding single, within‐test MA outliers in 21/194 PLPRP and 2/27 CLPRP tests. Median, intra‐subject, within‐test coefficients of variation (CVs) for PLPRP and CLPRP MA responses to informative agonists were acceptable for most agonists, with higher CV for 0.5 mg/mL ristocetin, weak agonists, and impaired responses, and only small differences between MA estimates across agonists. Between‐test agreement was good for MA responses, with 80.5% of patients and 98.1% of controls having consistent overall LTA findings, with significantly more patients than controls having consistently abnormal LTA findings (46.3% vs. 0%; p < 0.001). Conclusions Diagnostic LTA with LPRP has acceptable precision and reproducibility when evaluated with informative agonists. Nonetheless, caution is warranted when testing samples with ≤ 25 × 10 9 platelets/L, which often show BSS findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".