Predictive Factors for Discharge Destination Following Posterior Lumbar Spinal Fusion: A Canadian Spine Outcome and Research Network (CSORN) Study
Bibliographic record
Abstract
Study Design: Ambispective cohort study. Objective: Patients spend on average 3 to 7 days in hospital after lumbar fusion surgery. Patients who are unable to be discharged home may require a prolonged hospital stay while awaiting a bed at a rehabilitation facility, adding cost and imposing a considerable burden on the health care system. Our objective is to identify patient or procedure related predictors of discharge destination for patients undergoing posterior lumbar fusion. Methods: Analysis of data from the Canadian Spine Outcomes and Research Network. Patients who underwent lumbar fusion for degenerative pathology between 2008 and 2015 were identified. Multivariable logistic regression analysis was used to identify independent predictors of the discharge destination. Results: A total of 643 patients were identified from the database, 87.1% of the patients (N = 560) were discharged home while 12.9% (N = 83) required discharge to nonhome facilities. Using multivariate logistic regression analysis, the predictors for discharge to a facility rather than home were identified including: increasing age (odds ratio [OR] 1.045, 95% confidence interval [CI] 1.017 -1.075, P < .002), increasing body mass index (BMI) (OR 1.069, 95% CI 1.021 -1.118, P < .004), increasing disability score (OR 1.025, 95% CI 1.004 -1.046, P < .02), living alone preoperatively (OR 1.916, 95% CI 1.004-3.654, P < .05), increasing operating time (OR 1.005, 95% CI 1.003 -1.008, P < .0001), need for blood transfusion (OR 3.32, 95% CI 1.687-6.528, P < .001), and multilevel fusion surgery (OR 1.142, 95% CI 1.007 -1.297, P < .04). Conclusions: Older age, high BMI, living alone, high disability score, extended surgical time, blood transfusion, and multilevel fusion are significant factors that increase the odds of being discharged to facilities other than home. Level of Evidence: Level 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".