Surgical Rehospitalization of the Medicare Fee-For-Service Patient
Bibliographic record
Abstract
PURPOSE OF STUDY: Surgical readmissions are a concern to the integrity of the Medicare Trust Fund and gaining attention from policymakers concerned about solvency. This study explores factors associated with variation in surgical readmission rates across the states and provides implications for Medicare Case Management. PRIMARY PRACTICE SETTING(S): Acute inpatient hospital settings. METHODOLOGY AND SAMPLE: Fifty state-level data and multivariate regression analysis are used. The dependent variable Surgical Discharge 30-day Readmission Rate is based on the Medicare Fee-For-Service beneficiary population with Medicare Part A and B insurance coverage and age 65 years or older, rehospitalized subsequent to an inpatient surgical procedure, occurring within 30 days of their last discharge. RESULTS: Our 2 key explanatory variables-emergency room visit rate and total days of care-are each positively associated with 30-day surgical readmission rate. Age group 65-69 years, native language, physician density, and health care expenditures per capita also influence surgical readmission rate across the states. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Surgical readmission has an association with many different categories of variables-demographic, clinical process, hospital capacity, and patient need. This strongly suggests that Medicare case managers consider the wide range of elements contributing to surgical readmission and take a multifactorial approach to reducing the rehospitalization rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".