Estimation of the Harvey Bradshaw Index from the Patient-Reported Outcome 2 in Crohn’s Disease: Results Based on a Large Scale Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Many registrational trials in Crohn's disease assess treatment efficacy with the 2-item Patient-Reported Outcome (PRO2), while the Harvey-Bradshaw Index (HBI) is prominent in pragmatic trials and clinical practice. The translation between PRO2 and HBI has not been established. METHODS: Data from a Phase 3 trial of vedolizumab in Crohn's disease were used to determine the Pearson correlation between PRO2 and HBI. Linear regression was used to fit equations that estimate between indices; 95% prediction intervals were determined for HBI scores corresponding to PRO2 thresholds for disease activity. Internal validation of the conversion equations was performed using the bootstrap methods. RESULTS: PRO2 and HBI were highly correlated at baseline (r = 0.75 95% confidence interval (CI) 0.73-0.78; P < .001), induction (r = 0.87; 95% CI, 0.85-0.88; P < .001), and maintenance (r = 0.88; 95% CI, 0.85-0.90; P < .001). PRO2 and HBI change scores were moderately correlated (r = 0.72; 95% CI 0.69-0.75; P < .001) in induction and more strongly correlated during maintenance (r = 0.81; 95% CI 0.78- 0.84; P < .001). Regression equations for conversion of PRO2 to HBI from all cohorts (induction, maintenance, randomized, open-label) support an approximate conversion where HBI = 0.5 PRO2. As expected from the imperfect correlation between scores, the prediction intervals were generally wide. No evidence of overfitting was seen in bootstrap internal validation. CONCLUSIONS: PRO2 and HBI correlate strongly and conversion between them is possible. These findings facilitate the practical application of trial results and clinical guidelines.
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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 | 0.190 | 0.234 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".