PREDICTORS OF PROLONGED LENGTH OF STAY AFTER MAJOR FOOT AND ANKLE SURGERY
Bibliographic record
Abstract
Introduction Greater length of stay (LOS) after elective surgery results in increased use of health care resources and higher costs. Within the realm of foot and ankle surgery, improved perioperative care has enabled a vast majority of procedures to be performed as a day surgery. The objective of this study was to determine the perioperative factors that predict a prolonged LOS after elective ankle replacement or fusion. Methods Data was prospectively collected on patients undergoing either an ankle fusion or ankle replacement for end-stage ankle arthritis at our institution (2003–2010). In the analysis, LOS was the outcome and age, sex, physical and mental functional scores, comorbid factors, ASA grades, type and length of operation and body mass index (BMI) were potential perioperative risk factors. Univariate and multivariate generalized linear regression models with gamma distribution and log link function were conducted. Results A total of 336 patients were included in the study. The median LOS was 76 hours with interquartile range of 52.5–97. Using regression analysis, we showed aging, female gender, a higher ASA score, multiple medical comorbidities, rheumatoid arthritis, a lower score in the physical component (PCS) and general health domain (GH) of SF-36, open surgery and an increased length of surgical time were all significantly associated with an increased LOS. Conversely, obesity, the SF-36 Mental Component Score and the date of admission were noninfluential of LOS. A predictive model was also developed using these same risk factors. Conclusions Increased age, female gender, high ASA scores, low SF-36 GH and PCS scores, increased number of medical comorbidities, rheumatoid arthritis and open surgery were all factors that increased LOS significantly after ankle fusion or ankle replacement. This group of patients may warrant better education and more focused perioperative care when it comes to designing care pathways and allocating health care resources.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".