Central Endoscopy Reading in Inflammatory Bowel Diseases: Table 1.
Bibliographic record
Abstract
Endoscopic assessment of the presence and severity of endoscopic lesions has become an essential part of clinical trials in ulcerative colitis and Crohn's disease, for both patient eligibility and outcome measures. Variability in lesion interpretation between and within observers and the potential bias of local investigators in patient assessment have long been recognized. This variability can be reduced, although not completely removed, by independent evaluation of the examinations by experienced off-site (central) readers, properly trained in regard to lesion definition and identification, that should be removed from direct patient contact and blinded to any other clinical or study data. Adding endoscopic demonstration of active disease to eligibility criteria has the potential to reduce placebo response rates, whereas in outcome assessment it has the potential to provide a more precise estimation of the treatment effect, increasing the efficiency of the study. Central endoscopy reading is still at the beginning of its development, and the paradigms of central reading need refinement in terms of the number of readers, the process by which a final score is assigned, the selection and sequence of central readers, and the endoscopic indices of choice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".