Secondary prevention of cardiovascular disease: a randomised trial of training in information management, evidence-based medicine, both or neither: the PIER trial.
Bibliographic record
Abstract
BACKGROUND: Sub-optimal management of cardiovascular disease (CVD) patients is widespread in primary and secondary care, with risk factors frequently unrecorded or untreated. AIM: To investigate the effectiveness of educational interventions developed in primary care, on recording, prescribing and control of risk factors among all patients recorded by their general practitioner as having CVD. DESIGN OF STUDY: Factorial, duster-randomised controlled trial. SETTING: Primary care teams representing the range of practice development in a geographically defined area in inner London. METHOD: Participating practices were randomly allocated to one of the four intervention groups: information, evidence, both or neither. Interventions were tailored to suit individual practice needs. At a mean of 19 months after baseline, and three months after the end of intervention, practices carried out the follow-up assessment of recording, treatment, and control of risk factors in the same CVD patients. RESULTS: Adequate recording of all three risk factors, found inapproximately a third of patients at baseline, increased non-significantly by 10.5% (95% confidence interval [CI] = 3.9 to 24.9) in the information (versus not information) group and by 6.6% (95% [CI] = 8.9 to 22.0) in the evidence (versus not evidence) group. Factorial improvements in prescribing and control of risk factors tended not to be significant. Adequate recording of an three risk factors showed the greatest improvement in the information plus evidence group (19.9% increase, P for heterogeneity across the four groups < or = 0.001). Mean change from baseline to follow-up within the four intervention groups suggested improvements in the combined information plus evidence group in cholesterol recording (22.5% increase), prescribing of lipid lowering drugs (4.4% increase) and mean cholesterol (0.7 mmol/l decrease). CONCLUSIONS: Adequate risk factor recording did not differ between the information (versus not information) or the evidence (versus not evidence) intervention groups. Combined training in information systems and evidence-based medicine should be considered in the design of future interventions, to improve secondary prevention of CVD.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".