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 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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".