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Record W4417442786 · doi:10.1016/s2213-8587(25)00335-3

Application of continuous glucose monitoring and automated insulin delivery technologies for pregnant women with type 1, type 2, or gestational diabetes: an international consensus statement

2025· article· en· W4417442786 on OpenAlexaff
Katrien Benhalima, Celeste Durnwald, Arianne Sweeting, Dawn Adams, Ananta Addala, Tadej Battelino, Richard M. Bergenstal, Anders L. Carlson, Lois Donovan, Denise Reis Franco, Julie Heverly, Diana Isaacs, Anne‐Beatrice Kihara, Naomi Levitt, Carol J. Levy, Mareda Lewer, Elisabeth R. Mathiesen, Rimei Nishimura, Sarit Polsky, Uma Ram, David Simmons, Jennifer M. Yamamoto, Ádám G. Tabák, Denice S. Feig, Eleanor Scott

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2025
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of ManitobaLunenfeld-Tanenbaum Research InstituteUniversity of Calgary
Fundersnot available
KeywordsGestational diabetesPregnancyType 2 diabetesType 1 diabetesDiabetes mellitusInsulin resistanceRandomized controlled trialBlood Glucose Self-MonitoringInsulin

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.095
metaresearch head score (Gemma)0.075
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.075
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0040.003
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0100.005
Research integrity0.0160.020
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.024
GPT teacher head0.326
Teacher spread0.302 · 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
GenreMethods

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

Citations8
Published2025
Admission routes1
Has abstractno

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