Factors and Prediction Models for Unplanned Hospital Readmissions at a Pediatric Tertiary Centre
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it