Golimumab improves health-related quality of life of patients with moderate-to-severe ulcerative colitis: Results of the go-care study
Bibliographic record
Abstract
BACKGROUND: In recent years, improvement of Health-Related Quality of Life (HRQoL) in Ulcerative colitis (UC) has become a relevant measure for treatment efficacy. METHODS: We report results from a multicenter prospective study in Italy investigating HRQoL in adult patients with UC treated with golimumab (GLM). Patients who had shown clinical response after a 6-week induction phase (w0), were followed for an additional 48 weeks (w48) (total 54-week treatment). RESULTS: Of the 159 patients enrolled 90 completed the study. Compared to values at the beginning of treatment (n = 137), significant improvements were observed for mean total Inflammatory Bowel Disease Questionnaire (IBDQ) scores at w0 (168.5) and w48 (181.7). Patients with baseline PMS above the median tended to have greater improvements in IBDQ at w0 (OR 2.037, p = 0.033) and w48 (OR 3.292, p = 0.027). Compared to beginning of GLM treatment, the mean Full Mayo Score (FMS) decreased by 5.9 points at w48, while mean Partial Mayo Score (PMS) decreased by 3.9 points at w0 and by 4.9 points at w48. CONCLUSIONS: GLM improved HRQoL, disease activity and inflammatory biomarkers in UC patients with moderate-to-severely active disease. The greater the burden of disease activity at baseline, the greater the improvement of HRQoL after 24 and 48 weeks of treatment.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".