Anorectal emergencies: WSES-AAST guidelines
Bibliographic record
Abstract
Anorectal emergencies comprise a wide variety of diseases that share common symptoms, i.e., anorectal pain or bleeding and might require immediate management. While most of the underlying conditions do not need inpatient management, some of them could be life-threatening and need prompt recognition and treatment. It is well known that an incorrect diagnosis is frequent for anorectal diseases and that a delayed diagnosis is related to an impaired outcome. This paper aims to improve the knowledge and the awareness on this specific topic and to provide a useful tool for every physician dealing with anorectal emergencies.The present guidelines have been developed according to the GRADE methodology. To create these guidelines, a panel of experts was designed and charged by the boards of the World Society of Emergency Surgery (WSES) and American Association for the Surgery of Trauma (AAST) to perform a systematic review of the available literature and to provide evidence-based statements with immediate practical application. All the statements were presented and discussed during the WSES-AAST-WJES Consensus Conference on Anorectal Emergencies, and for each statement, a consensus among the WSES-AAST panel of experts was reached. We structured our work into seven main topics to cover the entire management of patients with anorectal emergencies and to provide an up-to-date, easy-to-use tool that can help physicians and surgeons during the decision-making process.
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.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".