Health selection into neighborhoods among patients enrolled in a clinical trial
Bibliographic record
Abstract
Health selection into neighborhoods may contribute to geographic health disparities. We demonstrate the potential for clinical trial data to help clarify the causal role of health on locational attainment. We used data from the 20-year United Kingdom Prospective Diabetes Study (UKPDS) to explore whether random assignment to intensive blood-glucose control therapy, which improved long-term health outcomes after median 10 years follow-up, subsequently affected what neighborhoods patients lived in. We extracted postcode-level deprivation indices for the 2710 surviving participants of UKPDS living in England at study end in 1996/1997. We observed small neighborhood advantages in the intensive versus conventional therapy group, although these differences were not statistically significant. This analysis failed to show conclusive evidence of health selection into neighborhoods, but data suggest the hypothesis may be worthy of exploration in other clinical trials or in a meta-analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".