Suspected Blood Indicator to Identify Active Gastrointestinal Bleeding: A Prospective Validation
Bibliographic record
Abstract
BACKGROUND: The suspected blood indicator (SBI) function in the RAPID Reader v8.3 program was designed to quickly identify the presence of blood in video capsule endoscopy. While previous retrospective studies have shown that the SBI function was accurate in detecting the presence of active bleeding in the small bowel, its specificity and sensitivity were poor. METHODS: An initial retrospective review (phase 1) compared 115 patients with active gastrointestinal bleeding seen on video capsule endoscopy (VCE) to 115 patients with no active bleeding seen on VCE to produce a highly accurate algorithm. A prospective study (phase 2) was then performed by applying the algorithm to 100 consecutive patients who received VCE for the following indications: obscure bleeding, iron deficiency anemia, melena, and hematochezia. RESULTS: The initial retrospective review found that eight contiguous SBI markers had a specificity of 100% in identifying active gastrointestinal bleeding regardless of the total number of SBI markers, while two or more contiguous SBI markers had a sensitivity of 96.5%. Using a cutoff of eight contiguous SBI markers, the prospective arm found that there was a 100% sensitivity and specificity in detecting active gastrointestinal bleeding (P < 0.001). CONCLUSIONS: The SBI function can greatly facilitate the identification of active gastrointestinal bleeding on VCE by using eight contiguous SBI markers as a cutoff for active bleeding.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".