A retrospective analysis of surgical, patient, and clinical characteristics associated with length of stay following elective lumbar spine surgery
Bibliographic record
Abstract
Elective spine surgeries consume significant hospital resources, contributing greatly to the costs of care. Length of stay (LOS) is the major cost driver for most hospitals. This study aimed to describe the demographic, clinical, operative, and postoperative characteristics of patients undergoing elective lumbar spine with wide-ranging rural discharge destinations, and how these characteristics are independently associated with LOS. This was a retrospective cohort study of patients at a quaternary institution undergoing surgery for single or two level lumbar degenerative conditions over a two year period. LOS was calculated as the number of days from the date of surgery to the date of discharge. A multiple quasi-Poisson regression was used to describe the characteristics independently associated with LOS. Surgery group was stratified when feasible to explore heterogeneity within the study population. A total of 473 patients met inclusion criteria. The median LOS was 3.0 days (Interquartile range (IQR) = 1–4) for the entire population, 4.0 days (IQR = 3–6) for 1-level transforaminal lumbar interbody fusion (TLIF) patients, 0 days (IQR = 0–1) for discectomy patients, and 2.0 days (IQR = 1–4) for laminectomy patients. Factors that were statistically significantly associated with LOS in adjusted analyses were age, BMI, preoperative antidepressant use, surgery group, long-acting intraoperative analgesics, operating surgeon, and postoperative blood transfusion. Stratified multivariable analysis showed effect modification by surgery group. LOS following elective lumbar spine surgery for degenerative conditions is associated with several patient, clinical, and surgical factors and is highly dependent on the type of surgical procedure performed. Building predictive models for anticipated LOS can help hospitals plan for ideal resource use and scheduling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".