Consensus on Training and Implementation of Enhanced Recovery After Surgery: A Delphi Study
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: Enhanced Recovery After Surgery (ERAS) is widely accepted in current surgical practice due to its positive impact on patient outcomes. The successful implementation of ERAS is challenging and compliance with protocols varies widely. Continual staff education is essential for successful ERAS programmes. Teaching modalities exist, but there remains no agreement regarding the optimal training curriculum or how its effectiveness is assessed. We aimed to draw consensus from an expert panel regarding the successful training and implementation of ERAS. METHODS: A modified Delphi technique was used; three rounds of questionnaires were sent to 58 selected international experts from 11 countries across multiple ERAS specialities and multidisciplinary teams (MDT) between January 2016 and February 2017. We interrogated opinion regarding four topics: (1) the components of a training curriculum and the structure of training courses; (2) the optimal framework for successful implementation and audit of ERAS including a guide for data collection; (3) a framework to assess the effectiveness of training; (4) criteria to define ERAS training centres of excellence. RESULTS: An ERAS training course must cover the evidence-based principles of ERAS with team-oriented training. Successful implementation requires strong leadership, an ERAS facilitator and an effective MDT. Effectiveness of training can be measured by improved compliance. A training centre of excellence should show a willingness to teach and demonstrable team working. CONCLUSIONS: We propose an international expert consensus providing an ERAS training curriculum, a framework for successful implementation, methods for assessing effectiveness of training and a definition of ERAS training centres of excellence.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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