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Record W4410728632 · doi:10.1177/19322968251340673

One Size Does Not Fit All: The Need for Sex-Specific Precision Medicine in Diabetes Technology

2025· article· en· W4410728632 on OpenAlexaff
Stefanie Hossmann, Susanne Tan, Julia K. Mader, David C. Klonoff, Dawn W. Adams, Hanne Ballhausen, Lia Bally, Maria L. Balmer, Vincent Braunack-Mayer, Anne Bonhoure, David Burren, Charlotte K. Boughton, Deniz Cengiz, Claudia Eberle, Chiara Fabris, Elke Fröhlich‐Reiterer, Tim Gunn, Olga Gusyatiner, Trevor Hastings, Valentina V. Huwiler, Saira Khan-Gallo, Carol J. Levy, Othmar Moser, Aisling Ann O’Kane, Cameron Keighron, Nick Oliver, Temiloluwa Prioleau, Tanja Thybo, Jane E. Yardley, Thomas Züger, G Faber-Heinemann, Lutz Heinemann, Martina Rothenbühler

Bibliographic record

VenueJournal of Diabetes Science and Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsGlycemicMedicineDiabetes mellitusAffect (linguistics)Precision medicinePregnancyMenopauseType 2 diabetesMEDLINEEndocrinologyPsychologyBiologyPathology

Abstract

fetched live from OpenAlex

Incorporating sex-specific factors in diabetes research and treatment is essential for advancing precision medicine. There are critical gaps in understanding and applying sex-related differences. Female-specific diabetes pathophysiology manifests in three major areas: life cycle phases (including puberty, pregnancy, and menopause), lifestyle factors (such as responses to nutrition and physical activity), and insulin pharmacology. These elements significantly affect insulin sensitivity and glycemic control in women, yet are frequently underrepresented or ignored in both research and clinical practice. Greater research and clinical focus across these domains is needed to better understand and address sex-based differences in diabetes. Identifying and filling evidence gaps will support more systematic and effective care.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.331
Teacher spread0.293 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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