P.225 Factors Contributing to Prolonged Length of Stay in Adults Undergoing Spine Surgery: Results from a Quaternary Spinal Care Center
Bibliographic record
Abstract
Background: Prolonged length of stay (LOS) is associated with increased resource utilization and worse outcomes. The goal of this study is identifying patient, surgical and systemic factors associated with prolonged LOS overall and per diagnostic category for adults admitted to a quaternary spinal care center. Methods: We performed a retrospective analysis on 13,493 admissions from 2006 to 2019. Factors analyzed included patient age, sex, emergency vs elective admission, diagnostic category (degenerative, deformity, oncology, trauma), presence of neurological deficits in trauma patients, ASIA score, operative management and duration, blood loss, and adverse events (AEs). Univariate and multivariate analyses determined factors associated with prolonged LOS. Results: Overall mean LOS (±SD) was 15.80 (±34.03) days. Through multivariate analyses, predictors of prolonged LOS were advanced age (p<0.001), emergency admission (p<0.001), advanced ASIA score (p<0.001), operative management (p=0.043), and presence of AEs (p<0.001), including SSI (p=0.001), other infections (systemic and UTI) (p<0.001), delirium (p=0.006), and pneumonia (p<0.001). The effects of age, emergency admission, and AEs on LOS differed by diagnostic category. Conclusions: Understanding patient and disease factors that affect LOS provides opportunities for QI intervention and allows for an informed preoperative discussion with patients. Future interventions can be targeted to maximize patient outcomes, optimize care quality, and decrease costs.
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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 | 0.004 |
| 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 | 0.000 |
| 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".