Interventions to Educate Family Physicians to Change Test Ordering
Bibliographic record
Abstract
The purpose is to systematically review randomised controlled trials (RCTs) to change family physicians' laboratory test-ordering. We searched 15 electronic databases (no language/date limitations). We identified 29 RCTs (4,111 physicians, 175,563 patients). Six studies specifically focused on reducing unnecessary tests, 23 on increasing screening tests. Using Cochrane methodology 48.5% of studies were low risk-of-bias for randomisation, 7% concealment of randomisation, 17% blinding of participants/personnel, 21% blinding outcome assessors, 27.5% attrition, 93% selective reporting. Only six studies were low risk for both randomisation and attrition. Twelve studies performed a power computation, three an intention-to-treat analysis and 13 statistically controlled clustering. Unweighted averages were computed to compare intervention/control groups for tests assessed by >5 studies. The results were that fourteen studies assessed lipids (average 10% more tests than control), 14 diabetes (average 8% > control), 5 cervical smears, 2 INR, one each thyroid, fecal occult-blood, cotinine, throat-swabs, testing after prescribing, and urine-cultures. Six studies aimed to decrease test groups (average decrease 18%), and two to increase test groups. Intervention strategies: one study used education (no change): two feedback (one 5% increase, one 27% desired decrease); eight education + feedback (average increase in desired direction >control 4.9%), ten system change (average increase 14.9%), one system change + feedback (increases 5-44%), three education + system change (average increase 6%), three education + system change + feedback (average 7.7% increase), one delayed testing. The conclusions are that only six RCTs were assessed at low risk of bias from both randomisation and attrition. Nevertheless, despite methodological shortcomings studies that found large changes (e.g. >20%) probably obtained real change.
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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 | 0.028 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".