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S0662 Evaluating the Optimum Number of Biopsies to Assess Histologic Inflammation in Ulcerative Colitis

2020· article· en· W3093742622 on OpenAlexaff
Robert Battat, Niels Vande Casteele, Rish K. Pai, Zhongya Wang, Guangyong Zou, John WD McDonald, Marjolijn Duijvestein, Jenny Jeyarajah, Claire E. Parker, Tanja van Viegen, Sigrid Nelson, Brigid S. Boland, Siddharth Singh, Parambir S. Dulai, Mark A. Valasek, Brian G. Feagan, Vipul Jairath, William J. Sandborn

Bibliographic record

VenueThe American Journal of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsWestern UniversityRobarts Clinical Trials
Fundersnot available
KeywordsMedicineBiopsyUlcerative colitisHistopathologyHistologyConfidence intervalInternal medicineGastroenterologyPathologyDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Histology is an emerging treatment target in ulcerative colitis (UC). We evaluated the minimum number of biopsies needed to accurately assess histologic activity in UC. METHODS: A convenience sample of UC patients with ≥4 rectosigmoid biopsies, and corresponding clinical and endoscopic data, was obtained from the University of California, San Diego biobank. Histology and endoscopic videos were evaluated by blinded central reading. A 4-biopsy Robarts Histopathology Index (RHI) reference score, consisting of the worst item-level ratings across the 4 biopsies, was compared to 1-, 2-, and 3-biopsy scores. Agreement was evaluated using bivariate errors-in-variable regression analysis (acceptance interval [AI]: ± 0.25 standard deviation of maximum reference score). Geboes (GS) and Visual Analog scale (VAS) estimates were similarly assessed. Subgroup analyses were performed according to endoscopic activity and disease location. RESULTS: A total of 46 patients had ≥ 4 rectosigmoid biopsies available. The 2-biopsy (tolerance interval [TI]: −7.66, 4.79) and 3-biopsy (TI: −4.86, 3.46) RHI scores agreed with the 4-biopsy score, whereas the 1-biopsy score did not (TI: −13.99, 7.78). Using 2-, 3-, and 4-biopsies yielded similar mean ± standard deviation RHI scores (6.1 ± 9.6, P = 0.36; 6.8 ± 10.5, P = 0.7; 7.5 ± 11.2, respectively) and identical histologic remission rates (65.2%, P = 1.0). Using 1 biopsy yielded numerically different mean RHI scores (4.4 ± 8.1, P = 0.06) and histologic remission rates (71.7%, P = 0.65). Similar findings existed for the VAS. For the GS, there was only agreement between the 3- and 4-biopsy scores (Table 1). For the RHI, agreement existed between 3- and 4-biopsies in both the endoscopically active and inactive subgroups whereas agreement between the 2- and 4-biopsies was observed in the endoscopically inactive, but not active, subgroup (Table 2). Scores based on rectosigmoid biopsies did not agree with those based on ascending or transverse colon biopsies (Table 3). Histologic remission rates were lower when biopsies from all three locations were used compared to the rectosigmoid alone (33.3% vs 61.9%, P = 0.1). CONCLUSION: A minimum of 2 biopsies accurately assesses UC disease activity in the rectosigmoid colon when using the RHI. Further studies of endoscopically active patients, and multiple sampling locations are needed. These findings have implications for biopsy sampling strategies both in clinical trials and routine practice.Table 1Table 2Table 3

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.027
GPT teacher head0.314
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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