Disease Activity and Health-related Quality of Life Relationships with Work Productivity in Patients with Ulcerative Colitis in OCTAVE Induction 1 and 2 and OCTAVE Sustain
Bibliographic record
Abstract
BACKGROUND: Tofacitinib is an oral small molecule Janus kinase inhibitor for the treatment of ulcerative colitis [UC]. We evaluated the relationship between Mayo/Inflammatory Bowel Disease Questionnaire [IBDQ] scores and Work Productivity and Activity Impairment-UC [WPAI-UC] components in patients with UC. METHODS: All available pooled data from three Phase 3 tofacitinib studies [OCTAVE Induction 1 and 2 and OCTAVE Sustain] were included. Relationships were estimated using repeated measures regression models with Mayo score/subscores or IBDQ total/domain scores as a separate anchor predictor and WPAI-UC components as the outcome. RESULTS: Evidence for linear relationships was confirmed between Mayo/IBDQ scores and WPAI-UC components. Robust relationships between total Mayo score/IBDQ total score and WPAI-UC presenteeism, work productivity loss, and activity impairment were observed; relationships with absenteeism were weak. Total Mayo scores of 0 and 12 corresponded, on average, to WPAI-UC component scores of < 15% and ≥ 60%, respectively, and IBDQ total scores of 224 and 32 corresponded, on average, to WPAI-UC component scores of < 6% and ≥ 90%, respectively. Presenteeism, work productivity loss, and activity impairment [all 0-100%], respectively, improved on average by 14.7, 13.6, and 16.4 percentage points for every 3-point improvement in total Mayo score, and by 8.1, 7.9, and 8.8 percentage points for every 16-point improvement in IBDQ total score. CONCLUSION: Robust relationships between Mayo/IBDQ scores with WPAI-UC presenteeism, work productivity loss, and activity impairment suggest that patient productivity and non-work activities are strongly associated with disease activity and HRQoL. The weak relationships with absenteeism suggest that patients attend work regardless of their disease activity/poor HRQoL. ClinicalTrials.gov: NCT01465763;NCT01458951;NCT01458574.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".