Investigating the effect of sociodemographic factors on 30-day hospital readmission among medical patients in Toronto, Canada: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the influence of patient-level sociodemographic factors on the incidence of hospital readmission within 30 days among medical patients in a large Canadian metropolitan city. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: Patients admitted to the General Internal Medicine service of an urban teaching hospital in Toronto, Canada participated in a survey of sociodemographic information. Patients were not surveyed if deemed medically unstable, receiving care in medical/surgical step-down beds or were isolated for infection control. Included in the final analysis was a diverse cohort of 1427 adult, non-palliative, patients who were discharged home. MEASURES: Thirteen patient-level sociodemographic variables were examined in relation to time to unplanned all-cause readmission within 30 days. Illness level was accounted for by the following covariates: self-perceived health status, previous hospital utilisation, primary diagnosis case mix group, Charlson Comorbidity Index score and inpatient length of stay. RESULTS: Approximately, 14.4% (n=205) of patients experienced readmission within 30 days. Sociodemographic factors were not significantly associated with time to readmission in unadjusted and adjusted analyses. Indicators of illness level, namely, previous hospitalisations, were the strongest risk factors for readmission within this cohort. One previous admission (adjusted HR 1.78; 95% CI 1.22 to 2.59, P<0.01) and at least four previous emergency department visits (adjusted HR 2.33; 95% CI 1.46 to 4.43, P<0.01) were associated with increased hazard of readmission within 30 days. CONCLUSIONS: Patient-level sociodemographic factors did not influence the incidence of unplanned all-cause readmission within 30 days. Further research is needed to understand the generalisability of our findings and investigate whether contextual factors, such as access to universal health insurance coverage, attenuate the effects of sociodemographic factors.
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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 | 0.002 | 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) | 0.002 | 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".