The Impact of Severe Obesity on Post-Acute Rehabilitation Efficiency, Length of Stay, and Hospital Costs
Bibliographic record
Abstract
Background and Objective. The purpose of this retrospective observational study was to examine the influence of severe obesity on length of stay (LOS), rehabilitation efficiency, and hospital costs post-acute rehabilitation in a population-based, tertiary care, publicly-funded regional rehabilitation center. Participants. 42 severely obese subjects (mean age 53 y; mean BMI 50.9 kg/m(2)) and 42 nonobese controls (mean age 59 y; mean BMI 23.0 kg/m(2)) matched by sex and admitting diagnosis. Main Outcome Measures. Total LOS, rehab LOS, waiting for transfer LOS, Fuctional Independence Measure (FIM) efficiency, and hospital costs. Results. Compared to controls, severely obese subjects experienced longer total LOS (98.4 vs. 37.4 days; P = 0.03), rehabilitation LOS (55.8 vs. 37.4 days; P = 0.04), and waiting for transfer LOS (42.6 vs. 0 days; P = 0.006); increased hospital costs ($115,822 vs. $43,969; P = 0.03); and similar FIM efficiency (0.58 vs. 0.67; P = 0.27). Severe obesity was an independent predictor of total LOS (beta-coefficient 0.51; P = 0.03), rehab LOS (0.46; P = 0.02) but not FIM efficiency (-0.63; P = 0.06). Conclusion. Severe obesity adversely affects rehabilitation LOS and expenditures. Targeted interventions in severely obese individuals to optimize post-acute rehabilitation care delivery are needed.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".