Vitamin B12 test volume data before and after the implementation of an educational province-wide intervention to reduce redundant testing in Alberta
Bibliographic record
Abstract
The data presented in this article is the provincial vitamin B12 test volume data for Alberta, Canada per month between April 1, 2015 and April 30, 2018. This data set was collected from the three different Alberta Public Laboratories Laboratory Information Systems: Cerner Millennium for Calgary, Sunquest for Edmonton, and MediTech for the remaining rural zones of Alberta (Bonnyville, Grand Prairie, Camrose, Red Deer, and Medicine Hat). An educational province-wide intervention aimed at reducing redundant testing was implemented on April 11, 2017 in Calgary, Alberta and Edmonton, Alberta and on May 2, 2017 in rural Alberta sites. All vitamin B12 test results in Alberta were appended with the educational comment "A normal test result indicates adequate stores and should not be repeated. However, if specific clinical situations require re-testing, the interval should not be sooner than 1 year." Provincial monthly test volumes prior to this intervention ranged from 54,182 to 73,522 tests per month and after this intervention ranged from 59,116 to 74,006 tests per month. The total number of vitamin B12 tests ordered over the 37 months in Alberta was 2,444,724; 690,448 tests were ordered in Calgary, 1,029,315 tests were ordered in Edmonton, and 724,961 tests were ordered in rural sites. This data article was submitted as a companion paper to the related research article, "Implementation of an educational province-wide intervention to reduce redundant vitamin B12 testing: a cross-sectional study"[1].
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".