Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes
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Bibliographic record
Abstract
BACKGROUND: The value of order sets for clinical decision support has not been established. OBJECTIVE: To determine whether introduction of admission order sets increases the proportion of inpatients receiving deep venous thrombosis (DVT) prophylaxis. DESIGN: Before-after study. SETTING: Community hospital. PATIENTS: General medical patients admitted to hospital. INTERVENTION: Paper-based admission order sets (instead of free-text orders) for voluntary use by internists, without any education or behavior change interventions. MEASUREMENTS: Primary outcome was proportion of medical admissions ordered DVT prophylaxis. Secondary outcomes included overall utilization of DVT prophylaxis in medical inpatients and other admission order care quality measures. RESULTS: Prior to introduction of order sets, DVT prophylaxis was ordered in 10.9% of patients. Patients admitted with order sets were more likely to be ordered DVT prophylaxis than patients admitted with free-text orders (44.0% versus 20.6%, by months 14 and 15, P<0.0001). Hospital-wide DVT prophylaxis in medical inpatients increased from 12.8% to 25.8% of patient-days (P<0.0001). Order set use improved many other secondary outcomes (P<0.05 for all), including allied health consultations (62.8% versus 12.7%), use of standardized diabetic diet (17.0% versus 5.1%), insulin sliding scale (19.1% versus 7.6%), potassium replacement protocol (63.8% versus 0.51%), documentation of allergies (54.3% versus 9.6%) and resuscitation status (57.4% versus 10.2%), and reduced orders for inappropriate laboratory tests such as blood urea nitrogen (39.4% versus 59.0%). CONCLUSIONS: The broad impact of order sets and minimal organizational resources required for their implementation suggests that order sets may have wide applicability as a clinical decision support tool.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it