Incidence and trend of type 1 diabetes and the underlying environmental determinants
Bibliographic record
Abstract
A wealth of epidemiological studies concerning the distribution of type 1 diabetes (T1D) around the world have pointed to the appreciable variation in the incidence of T1D among disparate age groups, ethnicities, and geographical locations. On the whole, the incidence of childhood T1D has been on the rise, and a plausible inverse relationship between the initial incidence rate and the following annual increase in incidence has been raised. Countries that used to exhibit lower incidences tend to have steep annual increase whereas those with already-established high incidences are more likely to show a modest increase or even stabilization in T1D incidence. Environmental agents considered responsible for the current evolving pattern of T1D incidence will be detailed, mainly including the increasing prevalence of childhood obesity, viral infections in a chronic manner, maternal-child interaction such as breastfeeding, and latitude-ultraviolet B-vitamin D pathway. Certain rationale has been put forward in an attempt to explain the potential association between environmental agents and development of T1D. For instance, accelerator hypothesis regards insulin resistance as the promoter of earlier disease onset in obese children whereas the negative correlation of microbial infections in background populations with incidence of T1D represents the basic component of the hygiene hypothesis. Further investigations are still warranted to verify these theories across multiple ethnic groups and to identify additional contributors to the variation in T1D incidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".