State of the Art Review: Evidence based management of acute appendicitis
Bibliographic record
Abstract
Introduction: Even though acute appendicitis is the world’s most common emergency general surgical operation, it remains under-researcher with wide variations in care. The aim of this review was to present current evidence on the management of acute appendicitis, focusing on risk assessment, diagnostic modalities, treatment strategies, and special considerations for specific patient populations. Methods: The writing group conducted a modified Delphi to prioritise topic areas for inclusion in this review. Consensus was achieved when each topic had >70% for either important or strongly important. Scoping reviews of current and grey literature were conducted to identify relevant evidence, focussing on new publications in the last 5 years (2019-2024). Results: Validated risk scoring systems, such as the Adult Appendicitis Score and the AIRS score, aid in identifying low-risk patients suitable for ambulatory management, while imaging modalities, including CT scans and ultrasound, play a pivotal role in confirming diagnosis and guiding treatment decisions. The review highlights the efficacy of surgical intervention versus antibiotic therapy, emphasising the importance of shared decision-making and individualised treatment plans. Tailored care strategies are needed for elderly patients, pregnant women, and those with appendiceal neoplasms whilst strategies for optimising antibiotic stewardship, minimising negative appendectomy rates, and enhancing postoperative care will provide the best evidence-based care. Discussion: This review provides evidence-based practices can be integrated into routine clinical care and ongoing education for frontline clinicians. The practice recommendations are designed to be evidence based and can be tailored depending on local resources. These should form the basis of future educational packages and surgical training programmes.
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".