Influence of Frailty on Clinical and Radiological Outcomes in Patients Undergoing Transforaminal Lumbar Interbody Fusion—Analysis of a Controlled Cohort of 408 Patients
Bibliographic record
Abstract
Background/Objectives: The concept of frailty has been recognized as an important issue which can influence postoperative outcomes. We aimed to investigate the influence of frailty on clinical and radiological outcomes in patients undergoing transforaminal lumbar interbody fusion (TLIF) for degenerative spine disease. Methods: A single-center, retrospective cohort study was conducted involving 408 patients in whom 506 expandable interbody devices were implanted. The patients were grouped into vulnerable/frail versus well/fit according to the Canadian Frailty Index. Results: The frail patients were older and had a larger number of fused segments (3.0 vs. 2.4 segments, p = 0.009). In the univariate analysis, the frail patients were more likely to experience a postoperative adverse event (AE) until discharge (OR 1.89, 95% CI 1.22–2.92; p = 0.004), three (OR 1.57, 1.07–2.3; p = 0.021), and 12 months postoperatively (OR 3.77, 1.96–7.24; p < 0.001). Following the multivariable logistic regression analysis, frailty remained an independent risk factor for postoperative AEs at 12 months (OR 3.44, 95% CI 1.69–6.99; p = 0.001). Conclusions: Frailty negatively influenced the rate of AEs until 12 months, while the odds of having a favorable outcome at any time remained unaffected in patients undergoing posterior spinal fusion with TLIF. Future efforts are needed to evaluate whether preoperative medical optimization or prehabilitation may positively impact patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".