Readmission following hypoxic ischemic brain injury: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Readmission to acute care is common and is associated with indicators of suboptimal care and health system inefficiencies. The objective of this study was to identify independent determinants of readmission following survival of hypoxic ischemic brain injury. METHODS: We conducted a population-based retrospective cohort study using Ontario's administrative health data. Survivors of hypoxic ischemic brain injury aged 20 years or more discharged from acute care between fiscal years 2002/03 and 2010/11 were included. Multivariable negative binomial regression was used to identify independent determinants of both number of readmissions and cumulative duration of hospital stay(s) within 1 year after the index discharge. RESULTS: Of the 593 patients with hypoxic ischemic brain injury, 233 (39.3%) were readmitted within 1 year of the index acute care discharge. The number of readmissions was associated with age (35-49 yr v. 65-79 yr: rate ratio [RR] 0.57, 95% confidence interval [CI] 0.38-0.85; ≥ 80 yr v. 65-79 yr: RR 0.58, 95% CI 0.34-0.97) and higher comorbidity score (Johns Hopkins Aggregated Diagnosis Groups score > 30 v. < 10: RR 1.60, 95% CI 1.11-2.31). Cumulative readmission stay was associated with increased index acute care length of stay (31-90 d v. ≥ 90 d: RR 4.17, 95% CI 1.38-12.64), prior use of health care services (minimal v. very high: RR 0.15, 95% CI 0.05-0.49) and discharge disposition (home v. continuing/long-term care: RR 0.44, 95% CI 0.21-0.91). INTERPRETATION: The findings indicate a high readmission rate in the first year after the index acute care admission for survivors of hypoxic ischemic brain injury, reflecting care gaps and system inefficiencies. This suggests that bolstered discharge and home care planning and support are needed to address the specific needs of those with hypoxic ischemic brain injury.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".