S797 Direct and Indirect Effects of Tofacitinib on Work Productivity in Patients With Ulcerative Colitis: A Mediation Analysis Between Work Productivity and the Mayo Score
Bibliographic record
Abstract
Introduction: Tofacitinib is an oral small molecule JAK inhibitor for the treatment of UC. Tofacitinib induction treatment has been shown to improve work productivity in patients (pts) with UC. However, it is unknown whether improvements in work productivity are fully explained by changes in the Mayo score or if other factors not captured by these changes contribute. Methods: We evaluated the interrelationship between Mayo score components (rectal bleeding [RB], stool frequency [SF], Mayo endoscopic subscore [MES], and Physician Global Assessment [PGA]) and Work Productivity and Activity Impairment-UC (WPAI-UC) components (overall work productivity loss and activity impairment) among pts in the Phase 3, 8-week induction studies that evaluated tofacitinib as induction therapy for UC (OCTAVE Induction 1&2; NCT01465763; NCT01458951). A mediation model used Mayo score components as mediators of the treatment effect on WPAI-UC components as outcomes. The MES was modelled as a predecessor of RB, SF, and PGA components as these are at least partially impacted by endoscopic inflammation. Work productivity loss and activity impairment can be viewed as complementary outcomes covering work and other daily activities, respectively. Analyses used all available pooled data from Week 8 in pts receiving tofacitinib/placebo. Results: There were 484 and 1,073 pts available for analysis in the models assessing work productivity loss and activity impairment, respectively. For the model evaluating work productivity loss, 100% of the impact of tofacitinib was mediated through Mayo score components, with the largest effect mediated by MES and PGA (46.8%; Figure a). For the model evaluating the effect of tofacitinib on activity impairment, 26.3% of the effect was mediated through factors not captured by the Mayo score, and 73.7% was mediated through Mayo score components; the largest effect was via MES and bowel-related symptoms, specifically SF (30.5%; Figure b). Conclusion: This analysis suggests the effects of tofacitinib on work productivity loss in pts with UC were fully mediated by Mayo score components, whereas the effects on activity impairment were only partially mediated by these. Bowel-related symptoms had the largest indirect effects in non-work environments. These findings provide important insights into the interrelationship between Mayo score components and WPAI-UC and may help inform healthcare providers on the impact of UC therapies on pts' work and leisure activities.Figure 1.: Summary of direct and indirect (mediated through Mayo score components) effects of tofacitinib vs placebo on the a) work productivity loss and b) activity impairment WPAI-UC components as a percentage of the total treatment effect. The WPAI-UC is a self-administered six-item questionnaire that generates four metrics: absenteeism (work time missed), presenteeism (impairment whilst working), productivity loss (overall work impairment from the combination of absenteeism and presenteeism), and activity impairment (non-work activity impairment). WPAI-UC component scores are expressed as percentages, with a higher percentage indicating greater impairment and less productivity. The Mayo score measures disease activity by assessing stool frequency, rectal bleeding, endoscopic appearance, and PGA. Total Mayo score ranges from 0 to 12 points (each subscore ranges from 0 to 3), with higher scores indicating more severe disease activity. MES, Mayo endoscopic subscore; PGA, Physician Global Assessment; WPAI-UC, Work Productivity and Activity Impairment-Ulcerative Colitis.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".