Prospective Cohort Retrospective Cohort •
Bibliographic record
Abstract
11Original research—Obesity and early reoperation rate after elective lumbar spine surgery: a population-based study The authors have no conflicts of interest to declare. This study was funded by Calgary Surgical Research Development Fund. This study has been approved by the University of Calgary Office for Medical Bioethics. Study design: Population-based retrospective cohort study. Clinical question: Are patients with a body mass index (BMI) of 35 or more who undergo elective lumbar spine surgery at increased risk of post-surgical complications, as evidenced by reoperation within a 3-month period? Methods: The Alberta Health and Wellness Administrative database was queried to identify patients who underwent elective lumbar spine surgery over a 24-month period. This same database was used to classify subjects as obese (BMI ≥35) and non-obese (BMI <35) and to determine who underwent repeated surgical intervention. The rate of reoperation was determined for both the obese and non-obese groups; further analyses were performed to determine whether certain subjects were at increased risk of reoperation. Results: The point estimate for relative risk for requiring reoperation was 1.73 (95 % confidence interval, 1.03–2.90) for obese subjects compared with non-obese subjects. The adjusted point estimate shows that deformity correction surgery is predictive for early reoperation while obesity is not. Conclusions: In obese subjects we observed an increased complication rate after elective lumbar spine surgery, as evidenced by reoperation rates within 3 months. When we considered other possible associations with reoperation, in adjusted analysis, deformity surgery was found to be pre-dictive of early reoperation. Final class of evidence-prognosis Study design
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.014 | 0.003 |
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".