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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".