Impact of Elevated Body Mass Index and Obesity on Long-term Surgical Outcomes for Patients With Degenerative Cervical Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Analysis of a combined prospective dataset. OBJECTIVE: To evaluate the impact of preoperative body mass index (BMI) on surgical outcomes in degenerative cervical myelopathy (DCM). SUMMARY OF BACKGROUND DATA: Although elevated BMI has been shown to have a deleterious impact on outcomes after lumbar spine surgery, limited evidence is available regarding its impact in DCM. METHODS: Analyses were completed using a combined North American/International prospective surgical DCM dataset from 26 participating centers. Outcome measures included Neck Disability Index (NDI), modified Japanese Orthopedic Association (mJOA) score, and Short Form- 36 (SF-36) scores at 1 year postoperatively. Bivariate and multivariable statistics were used to model the relationship between preoperative BMI, as both a continuous and categorical variable with these outcomes. RESULTS: Of 757 patients, mean BMI was 27.3 (±5.7) with 17 patients (3.5%) underweight, 271 patients (35.8%) normal weight, 275 patients (36.3%) overweight, and 194 patients (25.7%) obese. Controlling for preoperative mJOA, NDI, smoking status, age, and sex, elevated BMI was associated with increased neck disability at 1 year (P < 0.01). On average, NDI scores were 4.5 points higher (95% confidence interval, CI: 1.6-7.6) for overweight patients and 5.7 points higher (95% CI: 2.6-8.9) for obese patients compared with individuals of normal weight. Obese patients had 0.5 times odds (odds ratio, OR = 0.5, 95% CI: 0.3-0.8, P < 0.01) of showing improvement equal to the minimal clinically important difference of NDI compared with their normal weight counterparts. Although there were strong trends towards reduced SF-36 mental component scores and physical component scores with elevated BMI, no association was found between BMI and 1-year mJOA. CONCLUSION: Increased BMI, particularly obesity, was associated with increased postoperative disability. This represents a potentially modifiable risk factor which clinicians can target to optimize postoperative outcomes. LEVEL OF EVIDENCE: 2.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".