Elevated Patient Body Mass Index Does Not Negatively Affect Self-Reported Outcomes of Thoracolumbar Surgery: Results of a Comparative Observational Study with Minimum 1-Year Follow-Up
Bibliographic record
Abstract
Study Design Retrospective study. Objective Quantify the effect of obesity on elective thoracolumbar spine surgery patients. Methods Five hundred consecutive adult patients undergoing thoracolumbar spine surgery to treat degenerative pathologies with minimum follow-up of at least 1 year were included. Primary outcome measures included Numerical Rating Scales for back and leg pain, the Short Form 36 Physical Component Summary and Mental Component Summary, the modified Oswestry Disability Index, and patient satisfaction scores collected preoperatively and at 3, 6, 12, and 24 months postoperatively. Secondary outcome measures included perioperative and postoperative adverse events, postoperative emergency department presentation, hospital readmission, and revision surgeries. Patients were grouped according to World Health Organization body mass index (BMI) guidelines to isolate the effect of obesity on primary and secondary outcome measures. Results Mean BMI was 30 kg/m(2), reflecting a significantly overweight population. Each BMI group reported statistically significant improvement on all self-reported outcome measures. Contrary to our hypothesis, however, there was no association between BMI group and primary outcome measures. Patients with BMI of 35 to 39.99 visited the emergency department with complaints of pain significantly more often than the other groups. Otherwise, we did not detect any differences in the secondary outcome measures between BMI groups. Conclusions Patients of all levels of obesity experienced significant improvement following elective thoracolumbar spine surgery. These outcomes were achieved without increased risk of postoperative complications such as infection and reoperation. A risk-benefit algorithm to assist with surgical decision making for obese patients would be valuable to surgeons and patients alike.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".