Obesity and lumbar fusion: increased risk of blood loss
Bibliographic record
Abstract
Background: Several studies have demonstrated that obese patients are at increased risk of perioperative complication during lumbar spine surgery. Herein we quantify the association between blood loss and obesity during lumbar fusion. Methods: Outcomes were collected in the setting of a single center randomized control trial conducted among patients undergoing elective lumbar fusion. A univariate analysis of potential risk factors (gender, age, body mass index [BMI], number of levels fused, previous use of anticoagulants, and previous use of non-steroidal anti-inflammatories) for operative blood loss was performed. Logistic regression was conducted to estimate adjusted odds ratios (ORs) and 95% confidence intervals. Results: Among 85 patients, the mean estimated blood loss (EBL) was 563 ml, 47.1% were male, and the median number of levels fused was one. Obesity (BMI ≥30−kg/m2) was a significant risk (OR 2.46, P=0.025) for increased blood loss (EBL > 500 ml). Number of levels fused was similarly associated with EBL (P<0.01) while gender confounded the association between obesity and EBL. Conclusions: Surgeons should anticipate greater blood loss when performing lumbar fusion in obese patients. To reduce operative morbidity, consideration should be given to preoperative weight loss whenever possible.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".