Prescribing preoperative weight loss prior to major non-bariatric surgery for patients with elevated weight: a national provider survey (PREPARE provider survey)
Bibliographic record
Abstract
Background The surgical patient with obesity presents several challenges in intraoperative and postoperative care. We designed this cross-sectional survey to assess surgeon willingness to prescribe preoperative very low energy diets (VLEDs) and practice patterns in prescribing preoperative weight loss interventions for patients with obesity undergoing non-bariatric abdominal surgery. Methods and findings We conducted a cross-sectional survey of practicing surgeons in Canada who perform major non-bariatric abdominal surgery, reported in accordance with the Consensus-Based Checklist for Reporting of Survey Studies and utilizing non-probability convenience sampling. The primary outcome was willingness to prescribe preoperative VLED to obese patients undergoing major non-bariatric abdominal surgery for both benign and malignant indications. We created a multivariable proportional odds model to identify factors associated with willingness to prescribe VLEDs. A total of 78 participants completed and returned the survey (response rate 10.9%; mean age 43.54 ± 8.13 years; 48.72% female). Most surgeons (79.5%) felt that obesity significantly impacted the technical difficulty of their operations. We identified a disconnect between those surgeons who were willing prescribe VLEDs vs. those who actually prescribed them (78.2% vs. 30.8%, respectively). Approximately half of the surgeons reported being unfamiliar with VLEDs. Regression analysis identified practicing in academic institutions was associated with increased willingness to prescribe [odds ratio (OR) 3.71, 95% confidence intervals (CI) 1.01–13.7, p < 0.01]. Conclusion Although the majority of surgeons feel that obesity adversely impacts perioperative care, only one-third routinely discuss preoperative VLEDs with their patients. Opportunities to increase awareness and evaluate the impact of VLEDs on patient outcomes remain high.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".