Diagnostic Yield of Repeat Capsule Endoscopy and the Effect on Subsequent Patient Management
Bibliographic record
Abstract
BACKGROUND: Capsule endoscopy (CE) has been shown to produce a high diagnostic yield in patients with obscure gastrointestinal bleeding (OGIB); however, in those with negative studies, management is controversial. Very few studies have reported on repeat CE in the same patient; data regarding this diagnostic strategy are limited. OBJECTIVE: To determine the diagnostic yield of repeated CE studies and how this yield affects subsequent patient management. METHODS: A retrospective chart review of all patients who underwent CE at St Paul's Hospital (Vancouver, British Columbia) between December 2001 and June 2009 was conducted. Patients who underwent subsequent repeat CE were identified and divided into one of four subgroups. Findings were classified as positive or negative. RESULTS: Eighty-two of 676 patients underwent more than one CE study. Group 1 (incomplete study) included 22 patients (27%) and yielded 10 positive findings (45%). Group 2 (screening) comprised four patients (5%) and yielded two positive findings (50%). Group 3 (ongoing symptoms despite previous negative study) totalled 26 patients (32%) and yielded 10 positive findings (38%). Group 4 (previous positive study with treatment/investigation) included 30 patients (37%) and yielded 23 positive findings (77%). Overall, the present study found positive findings in 55% (45 of 82) of repeated CE cases, which resulted in a change in management in 39% (n=32) of the patients. CONCLUSION: Due to the high diagnostic yield and noninvasive nature of CE, repeat CE appears to be of benefit and should be considered for specific patients before other types of small bowel studies.
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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 | 0.007 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".