The current status of clinical trials in emergency gastrointestinal surgery: A systematic analysis of contemporary clinical trials
Bibliographic record
Abstract
BACKGROUND: Emergency gastrointestinal surgery (EGS) conditions represent a significant healthcare burden globally requiring emergency operations that are associated with mortality rates as high as 80%. EGS is currently focused on quality improvement and internal audits, which occurs at a national or local level. An appreciation of what EGS trials are being conducted is important to reduce research wastage and develop coordinated research strategies in surgery. The primary aim of this study was to identify and quantify recent and active trials in EGS. The secondary aim was to identify conditions of interest and which aspects of care were being modified. METHODS: A systematic search of WHO, UK, US, Australian, and Canadian trials databases was undertaken using broad terms to identify studies addressing emergency abdominal surgery and specific high-risk diagnoses. Studies registered between 2013 and 2018 were eligible for inclusion. Data on study topic, design, and funding body were collected. Interventions were classified into "perioperative", "procedural", "postoperative", "non-surgical", and "other" categories. RESULTS: Searches identified 5603 registered trials. After removal of duplicates, 4492 studies remained and 42 were eligible for inclusion. Almost 50% of trials were located in Europe and 17% (n = 7) in the United States. The most common condition addressed was acute appendicitis (n = 11), with the most common intervention being procedure based (n = 23). Hospital-based funding was the most common funder (n = 30). CONCLUSION: There is large disparity in the number of surgical trials in emergency surgery, which are primarily focused on high-volume conditions. More research is needed into high-mortality conditions. LEVEL OF EVIDENCE: Systematic review, level III.
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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 | 0.257 | 0.655 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.018 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".