Evolving Treatment Algorithms in Crohn's Disease
Bibliographic record
Abstract
BACKGROUND: Crohn's disease (CD) is a chronic, disabling and destructive condition. Half of patients will develop some bowel damage (stricture, fistula and/or abscess). Current therapeutic strategies failed to alter its natural history. OBJECTIVE: We explore in a review article the evolution of CD treatment over a quarter of a century from a linear sequence of treatment intensification to a complex algorithm focused on individualized patient care by looking beyond symptoms. Specifically we focus on evolving concepts in assessing disease severity, selecting rigorous treatment end-targets, initiating an effective therapeutic therapy, and managing secondary loss of response. RESULTS: A tight monitoring of objective signs of inflammation and a treat-to-target approach are probably the only way to change patients' life and disease course. We now seek to optimize our therapeutic tools according to patient profile, disease phenotype and the unique pharmacodynamics that ensues. CONCLUSION: Standardizing the clinical practice of gastroenteroogists with the most current treatment algorithm may minimize disease related complications while favouring patient's quality of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".