FAT AND NEUROSURGERY
Bibliographic record
Abstract
OBJECTIVE: Obesity has been linked to increased morbidity and mortality after some surgical procedures. The purpose of this study was to determine whether obesity affects outcome after general neurosurgery and subarachnoid hemorrhage (SAH). METHODS: Three data sets were analyzed, including a retrospective review of 404 patients undergoing cranial and spinal neurosurgical procedures, a prospective collection of 100 patients with aneurysmal SAH, and data from 3567 patients with aneurysmal SAH who were entered into randomized clinical trials of tirilazad. For each data set, outcome was assessed by mortality, postoperative morbidity, and Glasgow Outcome Scale score. Prognostic factors, including body weight and body mass index, were tested for their effect on these outcomes using multivariable logistic regression. RESULTS: For patients undergoing general cranial and spinal neurosurgery, independent predictors of morbidity and mortality were age, American Society of Anesthesia class, disseminated malignancy, emergency surgery, and increased duration of surgery. For patients with SAH, score on the Glasgow Outcome Scale was associated with age and admission Glasgow Coma Scale score. In the tirilazad data set, multiple factors were associated with score on the Glasgow Outcome Scale, but, as with the other 2 data sets, body weight had no relationship to outcome. CONCLUSION: Obesity may have less effect on the outcome of patients with mainly cranial neurosurgical disease and aneurysmal SAH than it does on patients undergoing other types of surgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.002 |
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".