Proceedings of resources for optimal care of acute care and emergency surgery consensus summit Donegal Ireland
Bibliographic record
Abstract
BACKGROUND: Opportunities to improve emergency surgery outcomes exist through guided better practice and reduced variability. Few attempts have been made to define optimal care in emergency surgery, and few clinically derived key performance indicators (KPIs) have been published. A summit was therefore convened to look at resources for optimal care of emergency surgery. The aim of the Donegal Summit was to set a platform in place to develop guidelines and KPIs in emergency surgery. METHODS: The project had multidisciplinary global involvement in producing consensus statements regarding emergency surgery care in key areas, and to assess feasibility of producing KPIs that could be used to monitor process and outcome of care in the future. RESULTS: Forty-four key opinion leaders in emergency surgery, across 7 disciplines from 17 countries, composed evidence-based position papers on 14 key areas of emergency surgery and 112 KPIs in 20 acute conditions or emergency systems. CONCLUSIONS: The summit was successful in achieving position papers and KPIs in emergency surgery. While position papers were limited by non-graded evidence and non-validated KPIs, the process set a foundation for the future advancement of emergency 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 | 0.091 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".