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Record W1984216175 · doi:10.4021/gr404w

Use of Capsule Small Bowel Transit Time to Determine the Optimal Enteroscopy Approach

2012· article· en· W1984216175 on OpenAlexvenueno aff
Brandon Chalazan

Bibliographic record

VenueGastroenterology Research · 2012
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEnteroscopyCapsule endoscopyMedicineDouble-balloon enteroscopyCapsuleGastroenterologyInternal medicineEndoscopy

Abstract

fetched live from OpenAlex

BACKGROUND: Capsule small bowel transit time (SBTT) is used to select the most effective enteroscopy approach when targeting capsule endoscopy (CE) findings. Aim of this study was to determine if capsule SBTT can be used to guide the choice of enteroscopy technique for reaching CE abnormalities. METHODS: Single center, retrospective study involving 60 patients. Data were abstracted from medical records of patients with abnormal CE who proceeded to enteroscopy which included push enteroscopy (PE) single balloon enteroscopy (SBE) and double balloon enteroscopy (DBE). RESULTS: Ninety five findings were documented on CE with presumed identification of 56 (59%) of these abnormalities by enteroscopy. Majority were angioectasias on CE (42%) and enteroscopy (59%). Optimal cutoff values for selection of enteroscopy procedure were: 0-21% SBTT for PE (80% sensitivity, 74% specificity, 83% PPV); 0 - 36% SBTT for antegrade SBE (93% sensitivity, 40% specificity, 82% PPV); 0 - 57% SBTT for antegrade DBE (75% sensitivity, 80% specificity, 75% PPV); and 74 - 100% SBTT for retrograde DBE (88% sensitivity, 78% specificity, 78% PPV). CONCLUSION: Capsule SBTT may be used to guide the selection of enteroscopy approach. PE, antegrade SBE, antegrade DBE and retrograde DBE are optimal when abnormalities on CE are seen at ≤ 21%, ≤ 36%, ≤ 57% and ≥ 74% SBTT respectively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.160
GPT teacher head0.345
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2012
Admission routes1
Has abstractyes

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