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Small bowel polyps and tumours: endoscopic detection and treatment by double‐balloon enteroscopy

2008· article· en· W2165589813 on OpenAlexaboutno aff
Lucía C. Fry, Helmut Neumann, Doerthe Kuester, Ralf Kühn, Michael Bellutti, Peter Malfertheiner, K. Mönkemüller

Bibliographic record

VenueAlimentary Pharmacology & Therapeutics · 2008
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnteroscopyDouble-balloon enteroscopyPeutz–Jeghers syndromeEndoscopyGastroenterologyIncidence (geometry)RadiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Double-balloon enteroscopy has allowed us not only to inspect deeply the small bowel but also to carry out interventions for diseases of the small bowel. AIM: To evaluate the utility of double-balloon enteroscopy for the diagnosis and therapy of these lesions. METHODS: All patients undergoing double-balloon enteroscopy for evaluation of small bowel polyps and tumours during a 3.75-year period at a university referral hospital were studied. The types of polyps and tumours as well as endoscopic technique of removal, surgery and complications were documented. RESULTS: The incidence of small bowel polyps and tumours in-patients undergoing DBE was 9.6%. A total of 40 double-balloon enteroscopy procedures were performed in 29 patients [13 female (44.8%), mean age 51 years, range 22-74]. The following lesions were found most frequently: adenomas in familial adenomatous polyposis syndrome, n = 8; hamartomas, n = 4 (Peutz-Jeghers and Cronkhite Canada syndromes), jejunal adenocarcinoma n = 5, neuroendocrine tumour n = 4 and others n = 6. CONCLUSIONS: The incidence of small bowel tumours in those in-patients who were undergoing double-balloon enteroscopy was 10%. Double-balloon enteroscopy is useful for the diagnosis and treatment of small bowel polyps and tumours.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.302
Teacher spread0.248 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations57
Published2008
Admission routes1
Has abstractyes

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