The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial
Why this work is in the frame
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Bibliographic record
Abstract
CONTEXT: Computerized drug alerts for psychotropic drugs are expected to reduce fall-related injuries in older adults. However, physicians over-ride most alerts because they believe the benefit of the drugs exceeds the risk. OBJECTIVE: To determine whether computerized prescribing decision support with patient-specific risk estimates would increase physician response to psychotropic drug alerts and reduce injury risk in older people. DESIGN: Cluster randomized controlled trial of 81 family physicians and 5628 of their patients aged 65 and older who were prescribed psychotropic medication. INTERVENTION: Intervention physicians received information about patient-specific risk of injury computed at the time of each visit using statistical models of non-modifiable risk factors and psychotropic drug doses. Risk thermometers presented changes in absolute and relative risk with each change in drug treatment. Control physicians received commercial drug alerts. MAIN OUTCOME MEASURES: Injury risk at the end of follow-up based on psychotropic drug doses and non-modifiable risk factors. Electronic health records and provincial insurance administrative data were used to measure outcomes. RESULTS: Mean patient age was 75.2 years. Baseline risk of injury was 3.94 per 100 patients per year. Intermediate-acting benzodiazepines (56.2%) were the most common psychotropic drug. Intervention physicians reviewed therapy in 83.3% of visits and modified therapy in 24.6%. The intervention reduced the risk of injury by 1.7 injuries per 1000 patients (95% CI 0.2/1000 to 3.2/1000; p=0.02). The effect of the intervention was greater for patients with higher baseline risks of injury (p<0.03). CONCLUSION: Patient-specific risk estimates provide an effective method of reducing the risk of injury for high-risk older people. TRIAL REGISTRATION NUMBER: clinicaltrials.gov Identifier: NCT00818285.
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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 | 0.017 | 0.022 |
| 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) | 0.000 | 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