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Record W3152494640 · doi:10.14309/ajg.0000000000001193

Accuracy of Screening Tests for Celiac Disease in Asymptomatic Patients With Type 1 Diabetes

2021· article· en· W3152494640 on OpenAlexaff
Michelle Gould, Farid H. Mahmud, Antoine Clarke, Charlotte McDonald, Fred Saibil, Zubin Punthakee, Margaret Marcon

Bibliographic record

VenueThe American Journal of Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsMcMaster UniversityHealth Sciences CentreSunnybrook Health Science CentreWestern UniversityUniversity of TorontoSickKids FoundationSt Joseph's Health CareHospital for Sick Children
Fundersnot available
KeywordsMedicineAsymptomaticTissue transglutaminaseSerologyType 1 diabetesPopulationCoeliac diseaseGastroenterologyInternal medicineBiopsyDiseaseDiabetes mellitusAntibodyImmunologyEndocrinology

Abstract

fetched live from OpenAlex

INTRODUCTION: To evaluate the diagnostic performance of celiac serologic tests in asymptomatic patients with type 1 diabetes (T1D). METHODS: Patients with T1D asymptomatic for celiac disease were prospectively screened with immunoglobulin A anti-tissue transglutaminase. Test characteristics were calculated and optimal cutoffs for a positive screen determined. RESULTS: Two thousand three hundred fifty-three patients were screened and 101 proceeded to biopsy. The positive predictive value of immunoglobulin A anti-tissue transglutaminase at the assay referenced upper limit of normal (30CU) was 85.9%, and the sensitivity and specificity were 100% and 38%, respectively. DISCUSSION: Thresholds extrapolated from the general population for the diagnostic evaluation of celiac disease are not suitable for use in asymptomatic T1D patients. Population-specific screening cutoffs are required.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2021
Admission routes1
Has abstractyes

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