Gastrointestinal recovery after surgery: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Gastrointestinal recovery after surgery is of worldwide significance. Postoperative gastrointestinal dysfunction is multifaceted and known to represent a major source of postoperative morbidity, however, its significance to postoperative care across all surgical procedures is unknown. The complexity of postoperative gastrointestinal recovery is poorly defined within gastrointestinal surgery, and even less so outside this field. To inform the clinical care of surgical patients worldwide, this systematic review and meta-analysis will aim to characterise the duration of postoperative gastrointestinal recovery that can be expected across all surgical procedures and determine the associations between factors that may affect this. METHODS AND ANALYSIS: MEDLINE, Embase, Cochrane Library and CINAHL will be searched for studies reporting the time to first postoperative passage of stool after any surgical procedure. We will screen records, extract data and assess risk of bias in duplicate. Forest plots will be constructed for time to postoperative gastrointestinal recovery, as assessed by various outcome measures. Because of potential heterogeneity, a random-effects model will be used throughout the meta-analysis. Funnel plots will be used to test for publication bias. Meta-regressions will be undertaken where the outcome is the mean time to first postoperative passage of stool, with potential predictors and confounders being patient characteristics, postoperative outcomes and surgical factors. ETHICS AND DISSEMINATION: This study will not involve human or animal subjects and, thus, does not require ethics approval. The outcomes will be disseminated via publication in peer-reviewed scientific journal(s) and presentations at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42021256210.
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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 | 0.057 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.009 |
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".