Neighbourhood environments and the risk of hospital admission for cardiometabolic and mental health comorbidities in multiple sclerosis: A population cohort analysis using linked administrative data
Bibliographic record
Abstract
This study exploits administrative data for neuroepidemiological research and examines associations between neighbourhood environments and risk of hospitalization among multiple sclerosis (MS) patients in New Brunswick, Canada. We created a provincial database of MS patients by linking administrative health records with geographic-based characteristics of local communities. Using Cox models, we found the risk of admission for cardiometabolic complications was lower among residents of ethnically homogeneous neighbourhoods (hazards ratio [HR]: 0.75 [95% confidence interval (CI): 0.60-0.95]); that for mental health disorders was higher in socioeconomically deprived (HR: 1.80 [95% CI: 1.06-3.05]) and residentially unstable (HR: 1.61 [95% CI: 1.05-2.46]) neighbourhoods. Results suggest that selected neighbourhood environments may be associated with differential hospital burden among MS patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".