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Diabetes Prevention Trial 1

2002· article· en· W1962844147 on OpenAlexaboutno aff
Liping Yu, David Cuthbertson, George S. Eisenbarth, Jeffrey P. Krischer

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesU.S. Public Health ServiceNational Institutes of Health
KeywordsOffspringAutoantibodyMedicineProbandFirst-degree relativesInternal medicineDiabetes mellitusType 1 diabetesPregnancyEndocrinologyImmunologyAntibodyBiologyFamily historyGenetics

Abstract

fetched live from OpenAlex

The Diabetes Prevention Trial Type 1 (DPT-1) has recruited relatives of patients with type 1 diabetes throughout the United States and Canada. Of the group screened before June 30, 2000, 71,148 initial screening samples of DPT-1 subjects were tested for GAD65 autoantibodies (GAA) and ICA512 (IA-2) autoantibodies (ICA512AA). Of 71,148 relatives screened, first-degree relatives (4.63%, n = 59,752) had a significantly higher prevalence of autoantibodies than did second-degree relatives (2.61%, n = 9,856) (P < 0.0001 for both autoantibodies). Among first-degree relatives, siblings (5.47%, n = 27,128) had a significantly higher prevalence of autoantibodies than did offspring (3.98%, n = 17,063) and parents (3.88%, n = 15,561) (P < 0.0001 for both autoantibodies). Among offspring, the offspring (n = 105) of both parents with diabetes had twice (8.57%) the prevalence of autoantibodies than did the offspring (n = 16,901) of a single diabetic parent (3.96%). Interestingly, the offspring (n = 8,777) of diabetic fathers had a significantly higher prevalence of autoantibodies than did the offspring (n = 8,124) of diabetic mothers, but only among those aged 10-30 years (P < 0.0001). We conclude that the prevalence of anti-islet cell autoantibodies is affected by multiple levels of relationship to the proband.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.315
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2002
Admission routes1
Has abstractyes

Explore more

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