Bibliographic record
Abstract
BACKGROUND: Computerized decision support systems (CDSSs) linked with electronic medical records (EMRs) are promoted as an effective means of improving patient care. However, very few high-quality studies are set in routine, community-based clinical care, and no consistent evidence of an effect on patient outcomes has been found. METHODS: A randomized controlled trial among EMR-using primary care practices in Ontario, Canada. Patients 55 years or older with previous vascular events, diabetes mellitus, hypertension, or hypercholesterolemia were randomized to the Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE III) CDSS intervention or to usual care. The intervention included personally tailored electronic vascular risk monitoring and treatment advice shared between the physician and patient, risk calculation, and a clinical resource. The primary outcome was a composite score of 8 recommended process outcomes at 1 year. Data collectors were blinded to group allocation. Analysis used the intention-to-treat principle with multiple imputation for missing data. RESULTS: We randomized and included in the analysis 1102 patients in 49 community-based physician practices (53.4% female; mean age, 69.1 years; 28.0% with a previous vascular event). The intervention group (545 [49.5%]) had a significantly greater improvement in mean process composite, with a difference of 4.70 (P < .001) on a 27-point scale. Intervention patients had significantly higher odds of rating their continuity of care (4.18; P < .001) and their ability to improve their vascular health (3.07; P < .001) as improved. Despite this improvement, the clinical outcomes-vascular events, clinical variables, and quality of life-were not improved. CONCLUSION: Despite favorable reviews and important improvements in the complex processes required to reduce vascular risk, clinical outcomes remain unchanged.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".