A scoping review of preoperative weight loss interventions on postoperative outcomes for patients with gastrointestinal cancer
Bibliographic record
Abstract
BACKGROUND: Obesity is associated with increased risk of surgical complications in some settings. OBJECTIVE: As a precursor to a systematic review, we conducted a scoping review of intentional preoperative weight loss to describe these interventions, their feasibility and effectiveness for patients with gastrointestinal cancer. METHODS: In April 2024, Ovid MEDLINE, EMBASE, CINAHL, and Google Scholar were searched for primary studies of intentional weight loss before elective gastrointestinal cancer surgery. Extracted data encompassed recruitment and attrition, intervention types, adherence, anthropometric and body composition changes, and surgical outcomes. Study quality was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. RESULTS: . Weight loss interventions included dietary modification (n = 3), exercise (n = 1), and combination (n = 3). None of the articles reported rates of recruitment, 2 adherence (97-100 %), and 4 reported attrition rates (0-18 %). All reported weight reductions of -1.3 to -6 kg and 4.5-6.9 % (n = 7), compared to baseline. Three of four non-randomized trials observed a reduction in postoperative complications, as compared to control; yet all trials were at critical risk of bias. CONCLUSION: Strong conclusions could not be made due to the limited reporting and critical risk of bias; further systematic review is not recommended at this time. To establish more robust evidence, there is a clear need for high-quality trials.
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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 | 0.024 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".