Inappropriate Repeats of Six Common Tests in a Canadian City: A Population Cohort Study Within a Laboratory Informatics Framework
Bibliographic record
Abstract
OBJECTIVES: To identify inappropriate repeats of six common laboratory tests in a population sample of patients, using highly specific criteria based only on repeat time and test value. METHODS: We used a laboratory informatics database to conduct a retrospective cohort study using a population sample of 103,000 patients in the city of Calgary with an index test in 2010 and uniform follow-up of 1 year. We examined six tests (cholesterol, hemoglobin A1c, thyroid-stimulating hormone, vitamin B12, vitamin D, and ferritin) with consensus-based or easily justified criteria for inappropriate repeats based solely on time to repeat and the index test value. RESULTS: The percentages of tests repeated at 3, 6, and 12 months were 11%, 23%, and 41%, respectively. In total, 16% of these six tests were inappropriately repeated, representing an annual internal cost of $0.6 to $2.2 million Canadian dollars and corresponding to population-scaled national estimates for Canada and the United States of $160 million and $2.4 billion, respectively. CONCLUSIONS: Objective definitions based on repeated testing identified 16% of six studied tests as inappropriate, delineating a subset of inappropriate testing that is well suited to automated identification and intervention and that provides a likely lower bound on the true burden of inappropriate testing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".