Bibliographic record
Abstract
STRIDE (Selecting Therapeutic Targets in Inflammatory Bowel Disease [IBD]) is an initiative by the International Organisation for the Study of IBD that aims to delineate a core set of therapeutic targets for IBD based on literature review and expert consensus. The first iteration was published in 2015, with an update in 2021 (STRIDE2), which qualifies targets as short-, intermediate- or long-term and adds pediatric-specific targets.
 The goal of treating any disease is to allow patients to feel well and to enjoy good quality of life (QOL), while avoiding disease- and treatment-related complications. The inflammatory bowel diseases, Crohn’s disease (CD) and ulcerative colitis (UC), are no exception. Given this overarching objective, it is not surprising that the traditional target in treating IBD has been symptom resolution, while avoiding corticosteroids. The challenge is that symptom control neither guarantees the absence of intestinal inflammation in a cross-sectional fashion, nor prevents progression to “damage” (including, for example, fibrosis, strictures and fistulae). This does not imply that symptom alleviation is irrelevant; it is a necessary, but insufficient treatment target. STRIDE2 includes clinical response (immediate/short-term) and clinical remission (intermediate) as treatment targets, but the method of symptom assessment has shifted from the physician (physician-administered clinical activity indices) to the patient (patient-reported outcomes [PROs]), aligning with the FDA’s requirement for PROs as a co-primary endpoint in clinical drug trials (typically alongside an objective disease marker such as endoscopy). STRIDE2 also introduces restoration of QOL and disability avoidance as key treatment goals. This further highlights the importance of the patient experience, and acknowledges normal linear growth as a critical pediatric-specific clinical target.
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 | 0.035 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.043 | 0.033 |
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".