A core outcome set for damage control laparotomy via modified Delphi method
Bibliographic record
Abstract
OBJECTIVES: Damage control laparotomy (DCL) remains an important tool in the trauma surgeon's armamentarium. Inconsistency in reporting standards have hindered careful scrutiny of DCL outcomes. We sought to develop a core outcome set (COS) for DCL clinical studies to facilitate future pooling of data via meta-analysis and Bayesian statistics while minimizing reporting bias. METHODS: A modified Delphi study was performed using DCL content experts identified through Eastern Association for the Surgery of Trauma (EAST) 'landmark' DCL papers and EAST ad hoc COS task force consensus. RESULTS: Of 28 content experts identified, 20 (71%) participated in round 1, 20/20 (100%) in round 2, and 19/20 (95%) in round 3. Round 1 identified 36 potential COS. Round 2 achieved consensus on 10 core outcomes: mortality, 30-day mortality, fascial closure, days to fascial closure, abdominal complications, major complications requiring reoperation or unplanned re-exploration following closure, gastrointestinal anastomotic leak, secondary intra-abdominal sepsis (including anastomotic leak), enterocutaneous fistula, and 12-month functional outcome. Despite feedback provided between rounds, round 3 achieved no further consensus. CONCLUSIONS: Through an electronic survey-based consensus method, content experts agreed on a core outcome set for damage control laparotomy, which is recommended for future trials in DCL clinical research. Further work is necessary to delineate specific tools and methods for measuring specific outcomes. LEVEL OF EVIDENCE: V, criteria.
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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 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".