REDucing Unnecessary Coagulation Testing in the Emergency Department (REDUCED)
Bibliographic record
Abstract
The PT/INR (prothrombin time/international normalized ratio) and aPTT (activated partial thromboplastin time) were tests developed in the early 20th century for specific and unique indications. Despite this, they are often ordered together routinely. The objective of this study was to determine if a multimodal intervention could reduce PT/INR and aPTT testing in the emergency department (ED). This was a prospective multi-pronged quality improvement study at St. Michael's Hospital. The initiative involved stakeholder engagement, uncoupling of PT/INR and aPTT testing, teaching, and most importantly a revision to the ED order panels. After changes to order panels, weekly rates of PT/INR and aPTT testing per 100 ED patients decreased (17.2 vs 38.4, rate ratio=0.45 (95% CI 0.43-0.47), p<0.001; 16.6 vs 37.8, rate ratio=0.44 (95% CI 0.42-0.46), p<0.001, respectively). Rate of creatinine testing per 100 ED patients, our internal control, increased during the same period (54.0 vs 49.7, rate ratio=1.09 (95% CI 1.06-1.12); p<0.0001) while the weekly rate per 100 ED patients receiving blood transfusions slightly decreased (0.5 vs 0.7, rate ratio=0.66 (95% CI 0.49-0.87), p=0.0034). We found that a simple process change to order panels was associated with meaningful reductions in coagulation testing without obvious adverse effects.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".