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Record W4224884339 · doi:10.14740/gr1487

Video Capsule Endoscopy in Gastroenterology

2022· review· en· W4224884339 on OpenAlexvenueno aff
Monjur Ahmed∥

Bibliographic record

VenueGastroenterology Research · 2022
Typereview
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCapsule endoscopyObscure gastrointestinal bleedingBowel preparationEndoscopyGastroenterologyGastrointestinal bleedingGeneral surgeryColonoscopyInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

Video capsule endoscopy (VCE) is a wireless technology used by gastroenterologists for various indications in their clinical practice. There has been significant improvement in this technology since its start about two decades ago. Specific video capsules have been made to evaluate the small bowel, colon, and esophagus. Now pan-enteric video capsule is available to assess both the small bowel and colon. VCE is a non-invasive procedure that has been tremendously evaluated for various gastrointestinal disorders, particularly small intestinal bleeding. There are specific contraindications and complications of VCE. This procedure has the technical part and video reading part. Newer software programs will come to reduce the reading time. Artificial intelligence is also coming for quick and accurate diagnosis of any positive findings during VCE. VCE is an important diagnostic test in the field of gastroenterology. Although it is an addition to optical endoscopic procedures to visualize the gastrointestinal mucosa, it has advantages and disadvantages.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.215
GPT teacher head0.457
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations19
Published2022
Admission routes1
Has abstractyes

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