Reducing inpatient heritable thrombophilia testing using a clinical decision-making tool
Bibliographic record
Abstract
AIMS: To evaluate the impact of a clinical decision-making tool, designed to educate physicians regarding heritable thrombophilia (HT) testing, on the volume of testing in hospitalised patients in the tertiary care setting. METHODS: We performed a retrospective cohort study over a 6-year period (2007-2012) at a single tertiary care centre intervention site and two regional control sites. In January 2010, the intervention site instituted a policy change whereby physicians ordering HT testing on inpatients needed to complete a pre-preprinted order (PPO) form that outlined the limitations of HT testing in the hospitalised setting. Failure to complete the PPO within 24 h resulted in test cancellation. Our main outcome measure was the volume of HT testing performed at the three study sites. RESULTS: Introduction of the PPO resulted in a 79.4% (95% CI 71.2% to 87.6%) reduction in factor V Leiden (FVL) testing at the intervention site. This decrease was significantly greater compared with those in the two control teaching hospitals over the same time periods (33.7% and 43.6%; both p<0.001). Reductions in FVL testing postintervention were observed among all ordering specialists. Similar postintervention reductions in testing volumes were observed for antithrombin (57.4%), protein C (61.9%) and protein S (62.2%) activity assays. CONCLUSIONS: In a large tertiary care hospital, the introduction of a clinical decision-making tool significantly reduced HT testing in inpatients across clinical specialties. The impact on patient outcome should be assessed in further studies.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".