An evaluation of methods for determining reference intervals for light transmission platelet aggregation tests on samples with normal or reduced platelet counts
Bibliographic record
Abstract
Light transmission platelet aggregation tests are important for diagnosing platelet function defects. However, uncertainties exist about the best procedures to determine aggregation reference intervals. We investigated methods for determining reference intervals for light transmission aggregation tests, using the % maximal aggregation values for prospectively collected data on healthy control samples. Reference intervals for samples tested at 250 x 10(9) platelets/l were determined by mean +/- 2 standard deviations and non-parametric analyses. To establish reference intervals for tests on thrombocytopenic subjects, regression analyses were used to estimate 95% confidence limits for % maximal aggregation, according to sample platelet counts, using data for control samples diluted to match the platelet count of undiluted thrombocytopenic patient platelet-rich plasma samples. For samples tested at 250 x 10(9) platelets/l, non-parametric analyses described 95% of data for healthy control samples better than mean +/- 2 standard deviations. For samples tested at lower counts, to match thrombocytopenic samples, the % maximal aggregation was influenced by platelet count and derived limits were wider at very low platelet counts for almost all agonists. With ristocetin, it proved feasible to test samples with very low platelet counts to exclude Bernard-Soulier syndrome and type 2B von Willebrand disease. Non-parametric analyses should be the preferred method to establish light transmission aggregation reference intervals for samples tested at normal platelet counts. The derived limits for thrombocytopenic samples provide guidance for evaluating thrombocytopenic platelet function disorders, including which agonists to test, based on the sample platelet count.
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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 | 0.138 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".