Evaluating the optimum number of biopsies to assess histological inflammation in ulcerative colitis: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The optimal ulcerative colitis biopsy protocol is unclear. AIM: To evaluate the number of biopsies required to accurately assess microscopic disease activity in ulcerative colitis METHODS: Biopsies from patients with ≥4 rectosigmoid samples, and clinical and endoscopic data, were retrospectively obtained from a prospective biobank. Histology and endoscopic videos were read blindly. A 4-biopsy Robarts Histopathology Index (RHI) reference score, consisting of the worst item-level ratings from four biopsies, was compared to 1-, 2- and 3-biopsy estimates. Agreement was determined using bivariate errors-in-variable regression analysis (acceptance interval: ±8.25). Endoscopic activity and disease location subgroup analyses were also performed. RESULTS: Forty-six patients had ≥4 rectosigmoid biopsies available (N = 287). The 2-biopsy (tolerance interval: -7.66, 4.79) and 3-biopsy (tolerance interval: -4.86, 3.46) RHI scores demonstrated acceptable agreement with 4-biopsy scores. One-biopsy scores demonstrated unacceptable agreement (tolerance interval: -13.99, 7.78). Mean RHI scores using the 2-, 3- and 4-biopsy approaches were similar (6.1 ± 9.6 P = 0.36; 6.8 ± 10.5, P = 0.7; 7.5 ± 11.2), whereas the 1-biopsy estimate was lower (4.4 ± 8.1, P = 0.06). Histological remission rates were identical for the 2-, 3- and 4-biopsy methods (65.2%, P = 1.0). Subgroup analysis demonstrated that three biopsies were required in patients with endoscopically active disease. Sampling additional colonic locations yielded lower histological remission rates compared to rectosigmoid sampling alone (33.3% vs 61.9%, P = 0.1). CONCLUSIONS: A minimum of two - conservatively, three - biopsies are required to reliably assess disease activity in a single colonic segment using the RHI. Further studies are needed of endoscopically active patients and sampling locations. These results have implications for biopsy strategies in clinical trials and practice.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".