Proteomic prediction of clinical response for a large multicenter clinical trial of Ulcerative AQ2 Colitis
Bibliographic record
Abstract
As a sub-study of the Ascend III clinical trial of mesalamine, patients participating in this large, international, multicenter, randomized, doubleblind, double dummy, active -controlled clinical trial were studied using serum proteomics before and during treatment (6 weeks). The Ascend III study was comprised of 772 patients with UC, and these patients were also given the opportunity to participate in a proteomic sub-study. Of these, consent for proteomic and genetic analysis was given by 659 subjects. We analyzed data for 284 females and 375 males, for at total of 1243 serum samples. Each serum sample was screened and quantified by a panel of 212 antibodies with the Multi-Analyte Profiling assay developed by Rules-Based Medicine, Inc. Paired samples (before and after 6 weeks of mesalamine treatment) were tested for each patient. Data was analysed using ANOVA and T-test comparisons. No Caption available. We identified a number of proteins that showed significant differences: Proteomic analysis of patients with UC can predict clinical response to mesalamine 2.4 or 4.8 grams per day or neither. Moreover, this work showed a gender difference in the proteomic patterns in responders and nonresponders that is independent to the dose of mesalamine used, suggesting different biologic processes in males and females with UC.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".