Prospective assessment of the effect on quality of life of anti‐tumour necrosis factor therapy for perineal Crohn’s fistulas
Bibliographic record
Abstract
BACKGROUND: Anti-tumour necrosis factor (TNF) therapy effectively treats Crohn's perineal fistulas (CPF); the effect on health-related quality of life (HRQoL) remains unknown. AIMS: To evaluate the effect of anti-TNF therapy on the HRQoL of patients with CPF in daily clinical practice. METHODS: Prospective evaluation of clinical and magnetic resonance imaging (MRI) responses, disease activity (Perianal Disease Activity Index - PDAI), and HRQoL assessment [Inflammatory Bowel Disease Questionnaire (IBDQ)] in patients receiving anti-TNF therapy for CPF treated up to 12 months. RESULTS: In all, 26 patients with CPF were treated (mean age 39 years; 19 infliximab, 7 adalimumab). At baseline, 85% patients had impaired IBDQ scores (mean 137; 'normal' >170). At 12 months, mean increases in IBDQ score for infliximab and adalimumab treated patients were 40 and 41 points respectively (P < 0.05). There were significant improvements in all IBDQ subscores (bowel, emotional, systemic, social) at 12 months (all P < or = 0.003). Fourteen patients (74%) on infliximab and six on adalimumab (86%) achieved IBDQ score > or =170. Mean increase in IBDQ score was 50, 34 and 16 points in patients with clinical fistula closure (P < 0.001), clinical response (P = 0.002) and no response (n = 1) respectively. IBDQ score increased for patients with MRI healing (P < 0.001) and MRI improvement (P = 0.016), but not for those with no MRI change (n = 2). IBDQ correlated significantly with PDAI at baseline and at 12 months. CONCLUSION: Anti-TNF therapy improves HRQoL in patients with CPF at 12 months and this improvement is most pronounced in patients with clinical and MRI healing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".