The SIFIPAC/WSES/SICG/SIMEU guidelines for diagnosis and treatment of acute appendicitis in the elderly (2019 edition)
Bibliographic record
Abstract
The epidemiology and the outcomes of acute appendicitis in elderly patients are very different from the younger population. Elderly patients with acute appendicitis showed higher mortality, higher perforation rate, lower diagnostic accuracy, longer delay from symptoms onset and admission, higher postoperative complication rate and higher risk of colonic and appendiceal cancer. The aim of the present work was to investigate age-related factors that could influence a different approach, compared to the 2016 WSES Jerusalem guidelines on general population, in terms of diagnosis and management of elderly patient with acute appendicitis. During the XXIX National Congress of the Italian Society of Surgical Pathophysiology (SIFIPAC) held in Cesena (Italy) in May 2019, in collaboration with the Italian Society of Geriatric Surgery (SICG), the World Society of Emergency Surgery (WSES) and the Italian Society of Emergency Medicine (SIMEU), a panel of experts participated to a Consensus Conference where eight panelists presented a number of statements, which were developed for each of the four topics about diagnosis and management of acute appendicitis in elderly patients, formulated according to the GRADE system. The statements were then voted, eventually modified and finally approved by the participants to the Consensus Conference. The current paper is reporting the definitive guidelines statements on each of the following topics: diagnosis, non-operative management, operative management and antibiotic therapy.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".