Bibliographic record
Abstract
OBJECTIVES: There is a need to develop and validate new metrics to access the appropriateness of laboratory test requests. METHODS: The mean abnormal result rate (MARR) is a proposed measure of ordering selectivity, the premise being that higher mean abnormal rates represent more selective test ordering. As a validation of this metric, we compared the abnormal rate of lab tests with the number of tests ordered on the same requisition. We hypothesized that requisitions with larger numbers of requested tests represent less selective test ordering and therefore would have a lower overall abnormal rate. RESULTS: We examined 3,864,083 tests ordered on 451,895 requisitions and found that the MARR decreased from about 25% if one test was ordered to about 7% if nine or more tests were ordered, consistent with less selectivity when more tests were ordered. We then examined the MARR for community-based testing for 1,340 family physicians and found both a wide variation in MARR as well as an inverse relationship between the total tests ordered per year per physician and the physician-specific MARR. CONCLUSIONS: The proposed metric represents a new utilization metric for benchmarking relative selectivity of test orders among physicians.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".