Estimating patient demographic profiles from practice location.
Bibliographic record
Abstract
OBJECTIVE: To test the accuracy of imputing a practice population's average socioeconomic characteristics (such as average education levels and average income) using census data centred on the location of the practice. DESIGN: Comparison of census data with survey data collected in primary care offices. SETTING: Ontario. PARTICIPANTS: A cross-sectional sample of patients from 116 urban practices. MAIN OUTCOME MEASURES: Patient data were compared with census data at different levels of aggregation using mean absolute relative error (ARE), median ARE, and Spearman rank correlations. RESULTS: A total of 4413 patient surveys were collected. Differences between patient profiles and census data were large. Most mean AREs were clustered between 0.70 and 0.80, and median AREs were as high as 1.67. Correlations were low (ρ = 0.02) to moderate (ρ = 0.48). These results held across both levels of aggregation. CONCLUSION: The use of imputation techniques based on practice location is inadvisable, given the large differences that were observed.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".