Readmission Rates and Determinants in a Higher-Risk In-patient GIM Population
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
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 it