Factors and Prediction Models for Unplanned Hospital Readmissions at a Pediatric Tertiary Centre
Bibliographic record
Abstract
Unplanned Hospital Readmissions (UHRs) are associated with increased morbidity and mortality, and may be preventable. This study identified factors associated with pediatric UHRs and developed prediction models. UHRs for pediatric patients from 2007-2009 and 2017-2019 at British Columbia Children’s Hospital were retrospectively reviewed. Factors for UHRs were analyzed, and prediction models were derived and tested. 5.26% (411/8387) of patients from 2007-2009 and 3.95% (329/8316) from 2017-2019 experienced at least one UHR. Varying by time period, factors for UHRs included: home health authority, age, previous ER visits, preadmission comorbidities, admission type, in-hospital interventions, and intensive care unit stay. Prediction models had areas under the receiver operating characteristic curve of .61 (2007-2009) and .67 (2017-2019). This study identified variables associated with UHRs. Differences in predictor variables between two time periods suggest that UHRs may not reflect quality of care, and future prediction models need to be iteratively refined.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".