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Record W4388671819 · doi:10.3389/fendo.2023.1270517

Following in Banting’s footsteps or straying from the path? Observations from contemporary diabetes innovation

2023· article· en· W4388671819 on OpenAlexaboutno aff
Connor Leadley, Ananta Addala, Juliet E. Berkeley, Hamish Crocket, Elizabeth A. Davis, Niranjala Hewapathirana, Sufyan Hussain, Rayhan Lal, K Lomax, Tom Wilkinson, Martin de Bock, Marie‐Anne Burckhardt

Bibliographic record

VenueFrontiers in Endocrinology · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMedical Research Council
KeywordsDiabetes mellitusEquity (law)MedicineQuarter (Canadian coin)GerontologyPolitical scienceEndocrinologyGeography

Abstract

fetched live from OpenAlex

While advancements in the treatment of diabetes continue to rapidly evolve, many of the newer technologies have financial barriers to care, opposing the egalitarian ethos of Banting who sold his patent on insulin for a nominal cost to allow it to be made widely available. Inequity in access to new therapies drives disparity in diabetes burden with potential for these gaps to widen in the future. The 2023 International Conference on Advanced Technologies and Treatments of Diabetes (ATTD) presented ground-breaking and current research in diabetes technology. Oral presentations of the ATTD conference 2023 were analyzed to describe what percentage of speakers discussed equity in their talks. Overall, less than a quarter of presenters discussed equity, though there was regional variation. To ensure that diabetes technologies reduce disparity and improve outcomes, we encourage future speakers at diabetes (technology) conferences to consider equity of diabetes care and incorporate this into their presentations.

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)0.024
metaresearch head score (Gemma)0.054
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.017
Scholarly communication0.0120.011
Open science0.0020.007
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0080.001

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 head0.094
GPT teacher head0.317
Teacher spread0.223 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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