Desempenho das diretrizes AGA, Fukuoka e Europeia nos incidentalomas mucinosos do pâncreas submetidos à ultrassonografia endoscópica com punção por agulha fina
Bibliographic record
Abstract
Introduction: Pancreatic cystic lesions are common and are not exclusively benign. There are 3 guidelines that help recommend surgery if there is a risk factor, sign of malignancy or follow the patient with imaging tests. Objective: To review and compare the performance of these guidelines in asymptomatic patients with mucinous neoplasms. Method: The literature review was carried out by collecting information published on virtual platforms in Portuguese and English. The material for reading and analysis was selected from the SciELO, Google Scholar, Pubmed and Scopus platforms. Initially, a search was carried out using the keywords “mucinous cystic neoplasm. pancreatic intraductal neoplasms. endoscopic ultrasound. fine needle aspiration” with AND or OR search, considering the title and/or abstract. Afterwards, considering only those that were most related to the topic, the full texts were read. Results: 37 articles were included. Conclusion: The European Guideline-DE-2018 proved to be more accurate for use in patients with asymptomatic mucinous neoplasia after the diagnosis obtained by EUS-PAF.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".