Elderly as a Predictor for Perioperative Complications in Patients Undergoing Multilevel Minimally Invasive Transforaminal Lumbar Interbody Fusion
Bibliographic record
Abstract
STUDY DESIGN: This was a single-institute retrospective study. OBJECTIVE: To describe perioperative and postoperative complications in elderly who underwent multilevel minimally invasive transforaminal lumbar interbody fusion (MIS TLIF) while identifying predictors of complications. SUMMARY OF BACKGROUND DATA: The number of elderly patients undergoing spinal fusion is rising. Spinal surgery in the elderly is considered high risk with high rates of complications. Perioperative and postoperative complications in elderly undergoing multilevel MIS TLIF is, however, not known. METHODS: A retrospective analysis was performed on 467 consecutive patients who underwent multilevel MIS TLIF at a single institution from 2013 to 2017. Two cohorts, 70 years or older and 50 to 69 years old were analyzed. Multiple logistic regressions with minor and major complication rates as the dependent variables were performed to identify predictors of complication based on previously cited risk factors. A p-value of 0.008 or less was considered significant. RESULTS: One hundred fifty-two elderly and 315 nonelderly patients underwent multilevel MIS TLIFs. The average age was 76.4 and 60.4 years for the elderly and nonelderly cohorts. We observed 13 major (8.44%) and 72 minor (47.4%) complications in the elderly. No difference was noted in complication rates between the cohorts, except for urinary tract infection (P = .004) and urinary retention (P = .014). There were no myocardial infarctions; hardware complications; visceral, vascular, and neural injuries; or death. Length of stay, comorbidity, and length of surgery were predictive of major and minor complications. CONCLUSION: Elderly may undergo multilevel MIS TLIF with comparable complication rates. Age was not a predictor of complications. Rather, attention should focus on evaluation of comorbidity and limiting operative times. LEVEL OF EVIDENCE: 3.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".