Use of Capsule Small Bowel Transit Time to Determine the Optimal Enteroscopy Approach
Bibliographic record
Abstract
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 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".