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S900 Response Trajectory by the Clinical Decision Support Tool Probability Groups in Vedolizumab-Treated Patients With Crohn’s Disease: A Pooled Analysis of GEMINI 2, VISIBLE 2, and VERSIFY

2023· article· en· W4387750822 on OpenAlexaff
Parambir S. Dulai, Giorgios Bamias, Vipul Jairath, Anthony Buisson, Marlies Neuhold, Dirk Lindner, Christian Agboton, Laurent Peyrin‐Biroulet

Bibliographic record

VenueThe American Journal of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineVedolizumabInflammatory bowel diseaseAbdominal painCrohn's diseaseInternal medicineDiseaseImputation (statistics)Physical therapyMissing dataGastroenterologyMachine learning

Abstract

fetched live from OpenAlex

Introduction: Previously, a clinical decision support tool (CDST) has been developed to support treatment decisions in vedolizumab (VDZ)-treated patients (pts) with Crohn’s disease (CD).1 The patient reported outcome PRO2, comprising the sum of the Crohn’s disease activity index (CDAI) scores for stool frequency and abdominal pain, and quality of life (QoL) are treatment targets for pts with CD.2 We evaluated the PRO2 and QoL response according to the CDST probability groups. Methods: Pooled analysis of data from pts treated with VDZ in the 3 clinical trials GEMINI 2, VISIBLE 2 and VERSIFY. Based on STRIDE-II, symptomatic response was defined as decrease in PRO2 ≥50% from baseline, and symptomatic remission as an average daily stool frequency ≤3 and an average daily abdominal pain ≤1.2 Inflammatory Bowel Disease Questionnaire (IBDQ) remission was defined as total IBDQ score of ≥170. The analysis included only timepoints assessed in all 3 studies. Nonresponse imputation was applied on missing data. Results: A total of 1190 pts were included in this analysis and were categorized at baseline according to CDST as having a low (n=241), intermediate (n=548) or high (n=401) probability of response to VDZ. Mean (SD) PRO2 score decreased from baseline in all 3 CDST groups, with the high probability group showing the fastest and greatest change from baseline (Figure 1A). Symptomatic response at Week 6 was achieved by 162 (40.4%), 131 (23.9%), and 23 (9.5%) pts in the high, intermediate, and low CDST groups, respectively. The rates of symptomatic response in the high group continued to exceed those of the low and intermediate groups through Week 52. The rates of symptomatic remission were greater in the high probability group at Week 6 and continued to exceed those of the low and intermediate groups through Week 52 (Figure 1B). Mean (SD) IBDQ total score increased from baseline in all 3 CDST groups (Figure 1C). In the high probability CDST group, 26 (6.5%), 140 (41.7%), 185 (46.1%) and 167 (44.3%) pts, respectively, were in IBDQ remission at baseline, Week 6, Week 26/30 and Week 52. Conclusion: Based on patient reported outcomes, CDST is able to predict the probability of symptomatic response and remission with faster onset and higher rates observed in the high CDST group. Fast and greater improvement in QoL is also seen in the high CDST group. References 1. Dulai PS et al. (2018) Gastroenterology 155:687-695.e10. 2. Turner D et al. (2021) Gastroenterology 160(5):1570-1583.Figure 1.: Mean PRO2 score (A), symptomatic remission (B), and mean IBDQ total score (C) by CDST probability group. CDST, clinical decision support tool; IBDQ, inflammatory bowel disease questionnaire; PRO, patient reported outcome. Panels A and C show observed data, panel B shows results based on nonresponse imputed data. PRO2 is the sum of the stool frequency and abdominal pain score of the Crohn’s disease activity index.

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)0.025
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.020
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.007
GPT teacher head0.264
Teacher spread0.257 · 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
Published2023
Admission routes1
Has abstractyes

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