Use of the Mean Abnormal Result Rate (MARR) to Gauge Changes in Family Physicians’ Selectivity of Laboratory Test Ordering, 2010-2015
Bibliographic record
Abstract
OBJECTIVES: The mean abnormal result rate (MARR) has recently been advanced as a metric of laboratory test appropriateness. We used the MARR metric to examine patterns of change in family physician test requisitions over time. METHODS: We accessed the Laboratory Information System of Calgary Laboratory Services for family physician-ordered testing on outpatients to gather aggregate test and abnormal result counts from 2010 to 2015. RESULTS: Over the 6 years, there was an annual average of 3,401,553 tests for 411,295 distinct patients on their first test requisition for the year. The MARR increased from 8.1% to 9.0% through this period. CONCLUSIONS: The MARR for Calgary and surrounding area gives tentative evidence of a gradual increase in physician test selectivity in recent years. Further data from other catchment areas are needed before making assertions about broader trends in physician awareness of laboratory resource use.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".