P.221 Frailty is an Important Predictor of 30-day Morbidity in Patients Treated for Lumbar Spondylolisthesis Using a Posterior Surgical Approach
Bibliographic record
Abstract
Background: A non-operative approach has been favoured for elderly patients with lumbar spondylolisthesis due to a perceived higher risk with surgery. However, most studies have used an arbitrary age cut-off to define “elderly.” We hypothesized that frailty is an independent predictor of morbidity after surgery for lumbar spondylolisthesis. Methods: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database for years 2010 to 2018 was used. Patients who received posterior lumbar spine decompression with or without posterior fusion instrumented fusion for degenerative lumbar spondylolisthesis were included. The primary outcome was major complication. Secondary outcomes were readmission, reoperation, and discharge to location other than home. Logistic regression analysis was done to investigate the association between outcomes and frailty. Results: There were 15 658 patients in this study. The mean age was 62.5 years (SD 12.2). Frailty, as measured by the Modified Frailty Index-5 was significantly associated with increased risk of major complication, unplanned readmission, reoperation, and non-home discharge. Increasing frailty was associated with increasing risk of morbidity. Conclusions: Frailty is independently associated with higher risk of morbidity after posterior surgery in patients with lumbar spondylolisthesis. These data are of significance to clinicians in planning treatment for these 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".