Performance Characteristics of Several Rules for Self-interpretation of Proficiency Testing Data
Bibliographic record
Abstract
CONTEXT: Proficiency testing (PT) participants can interpret their results to detect errors even when their performance is acceptable according to the limits set by the PT provider. OBJECTIVE: To determine which rules for interpreting PT data provide optimal performance for PT with 5 samples per event. DESIGN: We used Monte Carlo computer simulation techniques to study the performance of several rules, relating their error detection capabilities to (1) the analytic quality of the method, (2) the probability of failing PT, and (3) the ratio of the peer group SD to the mean intralaboratory SD. Analytic quality is indicated by the ratio of the PT allowable error to the intralaboratory SD. Failure of PT was defined (Clinical Laboratory Improvement Amendments of 1988) as an event when 2 or more results out of 5 exceeded acceptable limits. We investigated rules with limits based on the SD index, the mean SD index, and percentages of allowable error. RESULTS: No single rule performs optimally across the range of method quality. CONCLUSIONS: We recommend further investigation when PT data cause rejection by any of the following 3 rules: any result exceeds 75% of allowable error, the difference between any 2 results exceeds 4 times the peer group SD, or the mean SD index of all 5 results exceeds 1.5. As method quality increases from marginal to high, false rejections range from 16% to nearly zero, and the probability of detecting a shift equal to 2 times the intralaboratory SD ranges from 94% to 69%.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".