Sustainable Laboratory-Driven Method to Decrease Repeat, Same-Day WBC Differentials at a Tertiary Care Center
Bibliographic record
Abstract
OBJECTIVES: A CBC with WBC differential is often ordered when a CBC alone would be sufficient for patient care. Performing unnecessary WBC differentials adds to costs in the laboratory. Our objective was to implement a laboratory middleware algorithm to cancel repeat, same-day WBC differentials to achieve lasting improvements in laboratory resource allocation. METHODS: Repeat same-day WBC differentials were first canceled only on intensive care unit samples; after a successful trial period, the algorithm was applied hospital-wide. We retrospectively reviewed CBC with differential orders from pre- and postimplementation periods to estimate the reduction in WBC differentials and potential cost savings. RESULTS: The algorithm led to a monthly WBC differential cancellation rate of 5.40% for a total of 10,195 canceled WBC differentials during the cumulative postimplementation period (September 25, 2019, to December 31, 2020). Nearly all (99.94%) differentials remained canceled. Most patients only had one WBC differential canceled (range, 1-38). Savings estimates showed savings of $0.99 CAD per canceled differential and 1,060 minutes (17.7 hours) of technologist time. CONCLUSIONS: A middleware algorithm to cancel repeat, same-day WBC differentials is a simple and sustainable way to achieve lasting improvements in laboratory utilization.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".