Patients with ulcerative colitis who have normalized histology are clinically stable after de-escalation of therapy
Bibliographic record
Abstract
Abstract We have previously demonstrated that histological normalization in ulcerative colitis (UC) is associated with superior maintenance of remission outcomes. This single-center, retrospective case-control study assessed outcomes after the therapeutic de-escalation in UC patients who have achieved histologic normalization. A total of 111 patients were included, of which 24 underwent de-escalation, and 87 patients without therapeutic changes. The most commonly withdrawn therapy was aminosalicylates (50%), followed by immunomodulators (37.5%), and biologics (12.5%). Fourteen patients remained on therapies after de-escalation, including aminosalicylate (9/14), immunomodulators (3/14), and biologics (3/14), while 10 patients were not on any therapy immediately after withdrawal. Median follow-up was 43 months in the de-escalation group and 47 months in the control. The rates of clinical, endoscopic, and histologic recurrence were not significantly different between the two groups, nor was the proportion of patients who subsequently required additional therapies after withdrawal (P = 0.133). Clinical and endo-histologic recurrence rates were the lowest in patients who withdrew immunomodulators (0% and 14.3%, respectively). We demonstrate the clinical stability of therapeutic withdrawal in UC patients with histologic normalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".