Proceedings of resources for optimal care of acute care and emergency surgery consensus summit Donegal Ireland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it