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Record W4386388619 · doi:10.1007/s00592-023-02160-6

Correction to: Coexisiting type 1 diabetes and celiac disease is associated with lower Hba1c when compared to type 1 diabetes alone: data from the Australasian Diabetes Data Network (ADDN) registry

2023· erratum· en· W4386388619 on OpenAlexaff
Steven James, Lin Perry, Julia Lowe, Kim C. Donaghue, Anna Pham-Short, Maria E. Craig, Geoffrey Ambler, Kym Anderson, Sof Andrikopoulos, Jenny Batch, Justin Brown, Fergus Cameron, Peter G. Colman, Louise Conwell, Andrew Cotterill, Jennifer Couper, Elizabeth A. Davis, Martin de Bock, Jan Fairchild, Gerry Fegan, Spiros Fourlanos, Sarah J. Glastras, Peter Goss, Leonie Gray, Peter S. Hamblin, Paul L. Hofman, Dianne Jane Holmes‐Walker, Tony Huynh, Sonia R. Isaacs, Craig Jefferies, Stephanie Johnson, Timothy W. Jones, Jeff Kao, Bruce R. King, Antony Lafferty, Michelle Martin, Robert McCrossin, Kris Neville, Mark Pascoe, Ryan Paul, Alexia Peña, Liza Phillips, Darrell Price, Christine Rodda, David Simmons, Richard Sinnott, Carmel E. Smart, Monique Stone, Steve Stranks, Elaine Tham, Barbara J. Waddell, Glenn M. Ward, Benjamin J. Wheeler, Helen Woodhead, Anthony Zimmermann

Bibliographic record

VenueActa Diabetologica · 2023
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsType 2 diabetesDiabetes mellitusMedicineType 1 diabetesDiseaseInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

B value which is now updated as shown below coexistence of T1D and CD (B = -0.28; to -0.48 to -0.07 Body mass index has a small negative value in both the abstract and Table

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)0.003
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0780.041

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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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