Implementation of an intervention to reduce population-based screening for vitamin D deficiency: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: We describe the implementation of an intervention in Alberta in support of the Choosing Wisely Canada recommendation against population screening for vitamin D deficiency (as determined by serum total 25-hydroxyvitamin D testing). We hypothesized that the introduction of a specialized requisition for vitamin D testing would reduce the annual number of vitamin D tests performed. METHODS: We performed a cross-sectional observational study that included all vitamin D tests ordered in Alberta between Apr. 1, 2015, and Mar. 31, 2016. There were no exclusion criteria. A special requisition for ordering vitamin D tests in Alberta was introduced on Apr. 1, 2015. Using an interrupted time series model, we compared predicted versus observed vitamin D test volumes for the 12-month period following the introduction of the new requisition. The sole outcome measure was the monthly change in volume of vitamin D testing. In addition, we calculated any cost savings as a result of reduced testing. RESULTS: Over the first 12 months of the intervention, there was a reduction in the number of tests ordered from a predicted 342 477 tests to 29 525 tests (91.4% reduction). This decrease represented a direct spending decrease of Can$938 856-$1 564 760 per year in Alberta. INTERPRETATION: A provincially led implementation of a Choosing Wisely Canada recommendation resulted in a large and sustained reduction in serum total 25-hydroxyvitamin D testing in Alberta. This study shows that provincially led interventions based on Choosing Wisely Canada recommendations can result in substantial reductions in laboratory tests.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".