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Record W4296993478 · doi:10.3390/curroncol29100537

Issues and Prospects of Current Endoscopic Treatment Strategy for Superficial Non-Ampullary Duodenal Epithelial Tumors

2022· review· en· W4296993478 on OpenAlexvenueno aff
Tetsuya Suwa, Masao Yoshida, Hiroyuki Ono

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicGastrointestinal Tumor Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDuodenumEndoscopic mucosal resectionEndoscopic treatmentPolypectomyConfusionDissection (medical)Endoscopic submucosal dissectionLesionAdenomaDuodenoscopyGeneral surgeryEndoscopySurgeryInternal medicineColorectal cancerCancerColonoscopy

Abstract

fetched live from OpenAlex

An increasing number of duodenal tumors are being diagnosed over the years, leading to increased confusion regarding the choice of treatment options. Small-to-large tumors and histological types vary from adenoma to carcinoma, and treatment methods may need to be selected according to lesion characteristics. Because of its anatomic characteristics, complications are more likely to occur in the duodenum than in other gastrointestinal organs. Several reports have described the outcomes of conventional endoscopic mucosal resection, endoscopic submucosal dissection, cold snare polypectomy, underwater endoscopic mucosal resection, endoscopic full-thickness resection, and laparoscopic and endoscopic cooperative surgery for duodenal tumors. However, even in the guidelines set out by various countries, only the treatment methods are listed, and no clear treatment strategies are provided. Although there are few reports with a sufficiently high level of evidence, considering the currently available treatment options is essential. In this report, we reviewed previous reports on each treatment strategy, discussed the current issues and prospects, and proposed the best possible treatment strategy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.276
GPT teacher head0.501
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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