Green et al. Respond to “Clues to the Etiology of Inflammatory Bowel Disease”
Bibliographic record
Abstract
Dr. Moayyedi has made several insightful comments (1) on our paper (2). On the basis of his comments, we are encouraged to proceed with a number of follow-up studies, including modeling of the spatial relation between multiple sclerosis and enteric infections. We do, however, have the following comments in response. Firstly, Dr. Moayyedi suggests that migration of individuals between small geographic areas over time brings into question our conclusions concerning the relation between high levels of hygiene in earlier life and subsequent development of inflammatory bowel disease in later life (the “hygiene hypothesis”). While we agree that our study design may not have accurately captured information on the environmental characteristics of individuals in early life, we argue that this issue may not be as large a problem as it first appears. A number of recent studies have suggested that mobility rates have declined since the 1980s, with the majority of moves taking place over very short distances as people adjust their housing arrangements to accommodate life events such as marriage, divorce, and the addition of new children (3, 4). This would suggest that, barring dramatic changes in socioeconomic or life circumstances, most people when moving tend to migrate to adjacent neighborhoods with characteristics similar to those of the ones they just left. It is also plausible that exposure to enteric infections in both childhood and young adulthood may provide protection against autoimmune diseases later in life. Since there may be a relatively short period of time between the protective effect of enteric exposure and the peak incidence of inflammatory bowel disease, which occurs shortly thereafter in the third decade of life, the effect of migration on our study results may have been quite minimal. While the failure to control for the effects of migration in our study most certainly reduced spatial variability in disease rates and the strength of observed relations (4), the study produced marked and statistically significant results. Conceivably, if we had been able to successfully control for the migration bias in our study design, the spatial patterns and statistical relations we observed may have been more pronounced.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".