EAES and SAGES 2018 consensus conference on acute diverticulitis management: evidence-based recommendations for clinical practice
Bibliographic record
Abstract
BACKGROUND: Acute diverticulitis (AD) presents a unique diagnostic and therapeutic challenge for general surgeons. This collaborative project between EAES and SAGES aimed to summarize recent evidence and draw statements of recommendation to guide our members on comprehensive AD management. METHODS: Systematic reviews of the literature were conducted across six AD topics by an international steering group including experts from both societies. Topics encompassed the epidemiology, diagnosis, management of non-complicated and complicated AD as well as emergency and elective operative AD management. Consensus statements and recommendations were generated, and the quality of the evidence and recommendation strength rated with the GRADE system. Modified Delphi methodology was used to reach consensus among experts prior to surveying the EAES and SAGES membership on the recommendations and likelihood to impact their practice. Results were presented at both EAES and SAGES annual meetings with live re-voting carried out for recommendations with < 70% agreement. RESULTS: A total of 51 consensus statements and 41 recommendations across all six topics were agreed upon by the experts and submitted for members' online voting. Based on 1004 complete surveys and over 300 live votes at the SAGES and EAES Diverticulitis Consensus Conference (DCC), consensus was achieved for 97.6% (40/41) of recommendations with 92% (38/41) agreement on the likelihood that these recommendations would change practice if not already applied. Areas of persistent disagreement included the selective use of imaging to guide AD diagnosis, recommendations against antibiotics in non-complicated AD, and routine colonic evaluation after resolution of non-complicated diverticulitis. CONCLUSION: This joint EAES and SAGES consensus conference updates clinicians on the current evidence and provides a set of recommendations that can guide clinical AD management practice.
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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 | 0.188 | 0.306 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".