PREscribing preoperative weight loss prior to major non-bariatric abdominal surgery for patients with Elevated weight: Patient and Provider Survey Protocols (PREPARE surveys)
Bibliographic record
Abstract
BACKGROUND: Preoperative very low energy diet (VLED) interventions are used routinely in patients undergoing bariatric surgery, a surgical subspecialty that deals almost exclusively with patients with obesity. Yet, their use and study has been limited in non-bariatric abdominal surgery. To investigate the use of VLEDs in non-bariatric surgery, we plan on conducting a randomized controlled trial (RCT). Prior to proceeding, however, we have designed two surveys as important pre-emptive studies aimed at elucidating patient and provider perspectives regarding these interventions. METHODS: The patient survey is a cross-sectional, single-center survey aimed at assessing the safety, adherence, barriers to adherence, and willingness to participate in preoperative optimization protocols with VLEDs prior to undergoing elective non-bariatric intra-abdominal surgery (S1 File). The population of interest is all adult patients with obesity undergoing elective non-bariatric intra-abdominal surgery at St. Joseph's Healthcare Hamilton who were prescribed a course of preoperative VLED. The primary outcomes will be safety and adherence. The target sample size is 35 survey responses. The provider survey is a cross-sectional national survey of practicing surgeons in Canada who perform major non-bariatric abdominal surgery aimed assessing the willingness and ability to prescribe preoperative weight loss interventions amongst practicing Canadian surgeons who perform major non-bariatric abdominal surgery (S2 File). The population of interest is independent practicing surgeons in Canada who perform major non-bariatric abdominal surgery. The primary outcome will be willingness to prescribe preoperative VLED to patients with obesity undergoing major non-bariatric abdominal surgery for both benign and malignant indications. The target sample size is 61 survey responses. Descriptive statistics will be used to characterize the sample populations. To determine variables associated with primary outcomes in the surveys, regression analyses will be performed. DISCUSSION: These survey data will ultimately inform the design of an RCT evaluating the efficacy of preoperative VLEDs for patients with obesity undergoing major abdominal 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 | 0.074 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".