Postoperative Rehabilitation May Reduce the Risk of Readmission After Groin Hernia Repair
Bibliographic record
Abstract
Thirty-day readmission after surgery has been proposed as a quality-of-care indicator. We explored the effect of postoperative rehabilitation on readmission risk after groin hernia repair. We used the French National Discharge Database to identify all index hospitalizations for groin hernia repair in 2011. Readmissions within 30 days of discharge were clinically classified in terms of their relationship to the index stay. We used logistic regression to adjust the risk of readmission for patient, procedure and hospital factors. Among 122,952 index hospitalizations for inguinal hernia repair, 3,357 (2.7%) related 30-day readmissions were recorded. Reiterated analyses indicated that readmission risk was consistently associated with patient complexity: age (per year after 60 years, OR 1.03, 95% CI 1.02-1.03, P < 0.001), hospitalization within the previous year (OR 1.56, 95% CI 1.44-1.69, P < 0.001), and increasing severity and combination of co-morbidities. Postoperative rehabilitation was identified as a protective factor (OR 0.56, 95% CI 0.46-0.69, P < 0.001). Older patients and those with greater comorbidity are at elevated risk of readmission after inguinal hernia repair. Postoperative rehabilitation may reduce this risk. Further studies are warranted to confirm the protective effect of postoperative rehabilitation.
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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 | 0.010 |
| 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) | 0.001 | 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".