Perforated and bleeding peptic ulcer: WSES guidelines.
Bibliographic record
Abstract
Background:Peptic ulcer disease is common with a lifetime prevalence in the general population of 5-10% and an incidence of 0.1-0.3% per year. Despite a sharp reduction in incidence and rates of hospital admission and mortality over the past 30 years, complications are still encountered in 10-20% of these patients. Peptic ulcer disease remains a significant healthcare problem, which can consume considerable financial resources. Management may involve various subspecialties including surgeons, gastroenterologists, and radiologists. Successful management of patients with complicated peptic ulcer (CPU) involves prompt recognition, resuscitation when required, appropriate antibiotic therapy, and timely surgical/radiological treatment. Methods: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 board of the WSES 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 5th WSES Congress, and for each statement, a consensus among the WSES panel of experts was reached. Conclusions:The population considered in these guidelines is adult patients with suspected complicated peptic ulcer disease. These guidelines present evidence-based international consensus statements on the management of complicated peptic ulcer from a collaboration of a panel of experts and are intended to improve the knowledge and the awareness of physicians around the world on this specific topic. We divided our work into the two main topics, bleeding and perforated peptic ulcer, and structured it into six main topics that cover the entire management process of patients with complicated peptic ulcer, from diagnosis at ED arrival to post-discharge antimicrobial therapy, 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".