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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".