Bibliographic record
Abstract
We thank Dr. Mendall for his comments1 about our study describing the increasing average weight of patients with Crohn's disease in clinical trials over the years and the significant correlation between this novel finding and disease activity.2 This was a hypothesis-generating study of the data published in connection with previous clinical trials. The author states that fecal calprotectin is a useful surrogate marker for Crohn's disease risk and could thus be useful in future epidemiological studies. The role of fecal calprotectin in the association between obesity and Crohn's disease is a valid one. It was not possible to address this in our study of previous clinical trials of therapeutic agents. The associations ascribed to obesity, such as decreased physical activity3 and a high-fat diet,4 are ecological and do not impute causality with Crohn's disease. Adiposity in general may be proinflammatory as a result of multiple adipokine and cytokine effects. We agree that obesity might have a causal effect in Crohn's disease and could possibly affect its clinical outcome.2 Even in the absence of adiposity, a high-fat diet might accelerate disease pathogenesis in Crohn's disease,5 and alteration of the microbiome and barrier function could underlie this association.
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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.079 | 0.060 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".