Bibliographic record
Abstract
The T-cell mediated autoimmune process that destroys pancreatic beta cells in type 1 diabetes (T1D) is a complex phenotype influenced by multiple genetic and environmental factors. Human leukocyte antigen (HLA) accounts for about half of the genetic susceptibility, through a large variety of protective and predisposing haplotypes. Other important loci associated with T1D, with much smaller effects than HLA, include the insulin variable number of tandem repeats, PTPN22, and CTLA-4. Detecting the association and confirming it beyond doubt is only the first step. Identifying the functional variant from among a block of polymorphisms in tight linkage disequilibrium and determining its biological consequences can be an even more challenging task. It is hoped that the identification of additional loci and functional analysis of known ones, no matter how small each individual effect is, will provide: (1) pathophysiological insights necessary for the development of preventive interventions; (2) risk prediction to identify individuals that can benefit from them, and (3) potentially, identification of distinct subgenotypes, with different immune dysregulation pathways leading to the common disease phenotype that may respond to different preventive interventions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".