Predictors of Discharge Destination after Lumbar Spine Fusion Surgery
Bibliographic record
Abstract
Introduction Lumbar spine fusion surgery is a common surgical procedure used to treat a variety of lumbar spine conditions. A great number of patients fail to go home after surgery and require transfer to a rehabilitation center. Many Patients requiring transfer to rehabilitation centers often have an extended hospital length of stay due to lack of beds at rehabilitation centers. Each extra day spent at the hospital costs the health system approximately $1000 USD. The aim of this study was to identify predictive factors for discharging patients to an inpatient rehabilitation center after undergoing lumbar spine fusion surgery. Methods We retrospectively identified a total of 15,092 patients undergoing lumbar spine fusion from 2011 to 2013 using the ACS-NSQIP database. Patients were dichotomized based on discharge destination to patients who were discharged home ( N = 12,339) and others who were discharged to a rehabilitation center ( N = 2753). Outcomes included patient demographics, comorbidities, and clinical characteristics. A multivariate logistic regression was used to identify whether outcomes studied were predictive factors for discharging patients to a rehabilitation center after lumbar fusion surgery. Results Majority of patients were discharged home after lumbar fusion surgery (81.76%) with only some discharged to a rehabilitation center (18.24%). Multivariate analysis identified age ≥40, female gender, comorbidities (diabetes, COPD, CHF, and obesity), minor and major complications, hospital length of stay (LOS), operative time ≥ 259 minutes, multilevel surgery, and return to the operation room as significant predictive factors of discharging patients to a rehabilitation center after lumbar fusion surgery. Conclusion The identified predictive factors can help the health system in developing an algorithm for early recognition of patients requiring postoperative admission to a rehabilitation center and possibly decreasing their hospital LOS. This can significantly decrease the hospital costs for such patients.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".