Readmission Rates and Determinants in a Higher-Risk In-patient GIM Population
Bibliographic record
Abstract
Summary Unplanned readmission to hospital is a costly and frequent event. The authors sought to study readmission rates and determinants in a higher-risk in-patient general internal medicine population. They undertook a medical record review of discharges from such a unit. The chart review data were then linked to administrative discharge data and used to query for any readmission within 3 months or 1 year. The authors found that 219 in-patients were discharged alive. Of these, 51 (23.3%) were readmitted to a hospital within 3 months of discharge. In extended Kaplan-Meier analysis, there was a 47.6% readmission rate by 12 months after discharge. Important variables predicting readmission were liver disease, metastatic cancer, and a change in most responsible physician. The latter was a risk factor independent of length of hospital stay. The authors demonstrate that patients admitted to a general internal medicine service are at high risk for readmission to hospital. A change in the most responsible physician during the index admission is an independent risk factor for readmission. Processes around the transfer of care of patients between physicians may provide an opportunity for improvement in readmission rates and overall quality of care.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".