F.11 Predictors of length of stay following lumbar fusion
Bibliographic record
Abstract
Background: Accurate prediction of length of stay (LOS) following elective lumbar fusion may help optimize the utilization of resources and may assist with physician and patient expectations. Methods: Outcomes were collected prospectively among patients undergoing elective lumbar fusion. Prolonged LOS was defined as ≥5 days. The influence of baseline and peri-operative characteristics on the odds of prolonged LOS was assessed by a multivariate logistic regression model. Results: 150 patients underwent elective lumbar fusion surgery. Patient characteristics were as follows: average age was 61.9, average pre-operative back pain measured by the visual analogue scale was 54.3, and 36.5% of patients were identified as having severe disability, defined by an Oswestry disability index over 40. The average LOS was 4.9 days, with 28% having a prolonged LOS. Majority of patients had one level fused (69.7%). The odds of prolonged LOS were increased by severe disability (odds ratio [OR] 3.58, p<0.005), levels fused (OR 2.52, p<0.005), greater than 70 years of age (OR 3.81, p<0.005), and any treatment related adverse event (OR 4.32, p <0.02). There was no significant influence of prolonged surgery (p=0.3) or pre-operative back pain (p=0.23) on LOS. Conclusions: Prolonged length of stay was significantly influenced by severe disability, levels fused, age > 70, and any adverse events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.007 | 0.001 |
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".