Can you use a sequential sample of patients as a substitute for a full practice audit
Bibliographic record
Abstract
OBJECTIVE To compare rates of mammography screening among women in family practices, based on a sequential sample of eligible women presenting to the practices during an 8-week period, with rates found in a full audit of all eligible patients. DESIGN Chart review. SETTING Twenty community-based family practices in south-central Ontario. PARTICIPANTS Family physicians and their female patients 52 to 71 years old who had had at least 1 visit to the office during the past 3 years. INTERVENTION Eligible patients were sampled by 2 approaches: sequential sampling of patients coming for appointments during an 8-week period and a full practice audit of all eligible women. MAIN OUTCOME MEASURE Mammography rates found using the 2 approaches. RESULTS The mean time-appropriate rate of mammography screening based on the sequential sample was 66.4%. The mean time-appropriate rate of mammography screening for the full practice audit was 58.8%. The sequential sample rate was higher than that of the full audit by 7.6%; differences ranged from −6.5% to 24.9% among practices. Regression analysis indicated a positive and significant correlation between rates based on the data generated by the 2 different approaches ( r 2 = 0.50). CONCLUSION A rate of mammography screening based on a sequential sample can reasonably approximate the actual rate of mammography screening that would be found based on a full practice audit.
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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 | 0.041 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".