Estimation of genetic risk for type 1 diabetes
Bibliographic record
Abstract
The most important gene loci defining risk of type 1 diabetes mellitus (T1DM) are located within the HLA gene region. HLA-DQ molecules are of primary importance but HLA-DR gene products modify the risk conferred by HLA-DQ. The risk associated with an HLA genotype is defined by the particular combination of susceptible and protective alleles. The highest risk is associated with a combination of two different risk haplotypes (7% risk to develop T1DM in Finland) whereas protective genotypes covering 69% of population have a risk of less than 0.2%). The complicated analysis of HLA genotypes is simplified by strong linkage disequilibrium between HLA-DRB1, -DQA1 and -DQB1 loci. In many cases one can deduce the alleles of other loci based on determination of the alleles in one locus. Differences between various populations in the frequency of marker alleles and in the linkages between them has to be taken into account. We have developed PCR based typing methods that utilize blood spot samples, microtiter plate format and lanthanide labeled oligonucleotide probes to define HLA-DQ and -DR alleles relevant for T1DM risk. Typing is run stepwise so that after initial HLA-DQB1 typing only those samples will be further analyzed in which -DQA1 or -DRB1 typing is informative and expected to contribute to the risk estimation. This method has been used to screen more than 50,000 newborn infants in Finland over a time period of 6 years, and it has been able to identify most children who have developed T1D during the follow-up period. The efficiency of the procedure has also been tested in Finnish and Greek populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".