2nd Mayo/NASCOLA Coagulation Testing Quality Conference
Bibliographic record
Abstract
Platelet aggregation testing is important to evaluate platelet function disorders. Historically, reference intervals (RI) for percentage aggregation responses are derived using the mean 2 SD (MSD) for healthy volunteer samples. However, a nonparametric approach (NP) is acceptable for RI determination, especially if there is more than one observation per individual or non-normal distribution of data. We compared MSD and NP approaches to determine platelet aggregation RI. Methods: Data on % aggregation by light transmission was prospectively collected for platelet rich plasma (250 3 10 9 platelets/L) from healthy controls (some tested multiple times), stimulated with (final concentrations): 2 and 4 lM ADP, 1.25 and 5 lM Horm collagen, 1.6 mM arachidonic acid, 1 lM thromboxane analogue, and 0.5 and 1.25 mg/mL ristocetin. RI were estimated by MSD and NP using ''all'' data and ''first'' measurements, with 2.5% of data outside lower or upper RI considered acceptable. Results: As the % aggregation responses to most agonists had non-normal distributions, RI by MSD was not appropriate particularly for ADP and low dose ristocetin which showed dramatic deviations from a normal distribution. The use of all or first measurements generated similar RI by NP but not by MSD. In addition, NP RI were not dependent on data distribution, resulting in more acceptable proportions of values above or below RI than with MSD (% above or below RI using ''all'' ; ''first'' measurements: NP 0.94-2.21%; 1.33-2.13%, MSD 0.88-6.36%; 0.00-5.52%). Conclusion: RI determination by a nonparametric approach generated the most representative RI for % aggregation and it had the advantage of using all available repeated measurements, without dependency on data distribution. Because the nonparametric approach is also recommended when only a limited numbers of controls are tested, we suggest that it be the preferred approach for establishing platelet aggregation RI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".