The impact of inpatient capsule endoscopy on the need for therapeutic interventions in patients with obscure gastrointestinal bleeding
Bibliographic record
Abstract
BACKGROUND/AIM: There are limited data evaluating the impact of inpatient video capsule endoscopy (VCE) on the need for therapeutic interventions in hospitalized patients with obscure gastrointestinal bleeding (OGIB). The objective of this study was to determine the impact of inpatient VCE on the need for therapeutic interventions and rehospitalization for recurrent bleeding. PATIENTS AND METHODS: Hospitalized patients who underwent VCE for OGIB indication were retrospectively included. Clinical data were collected including therapeutic interventions performed after VCE. Specific therapeutic interventions were defined as the medical, endoscopic, or surgical treatment directly targeting the cause of OGIB. Patients were followed up to determine the rate of rehospitalization. RESULTS: A total of 48 inpatient VCE were identified, of which 43 VCE were performed for OGIB indication and were included for analysis. The completion rate and the diagnostic yield were 78.5% and 55.8%, respectively. Subsequent specific therapeutic interventions were performed in 65.2% and 5.8% of patients with positive and negative VCE, respectively (P < 0.001). After a median follow up of 30 months (minimum 12, maximum 58), rehospitalization for recurrent bleeding occurred in 30.4% and 17% of patients with positive and negative VCE, respectively. Patients with angiodysplasia on VCE were significantly more likely to be readmitted (P = 0.02). Throughout the course of the follow-up, only 2 (11.7%) patients with negative VCE underwent specific therapeutic interventions. CONCLUSION: Inpatient VCE is an effective tool to identify patients who need specific therapeutic interventions. Patients with negative VCE are unlikely to be readmitted or require specific therapeutic interventions in the index admission.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".