MétaCan
Menu
Back to cohort
Record W4309648547 · doi:10.1016/s2589-7500(22)00197-2

Treating type 2 diabetes: moving towards precision medicine

2022· letter· en· W4309648547 on OpenAlexaff
Oriana Hoi Yun Yu, Juyoung Shin

Bibliographic record

VenueThe Lancet Digital Health · 2022
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsType 2 diabetesPrecision medicineMedicineDiabetes mellitusComputer scienceEndocrinologyPathology

Abstract

fetched live from OpenAlex

Type 2 diabetes is a prevalent condition with a rate of 6059 cases per 100 000 globally reported in 2017, and forecasted to increase to 7079 individuals per 100 000 by 2030.1 Several glucose-lowering medications have been developed to treat people with type 2 diabetes. Numerous guidelines2–5 for type 2 diabetes management recommend metformin as a first-line treatment, along with healthy lifestyle behaviours. Although guidelines provide recommendations on some second-line treatments for people with type 2 diabetes when metformin is contraindicated or insufficient to lower glycaemia, apart from comorbidities (eg, obesity, history of cardiovascular disease, and renal disease), there are other factors (such as renal insufficiency, medication costs, and patient preferences) that clinicians need to consider when selecting a second-line treatment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.165
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.324
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Digital HealthSame topicDiabetes Treatment and ManagementFrench-language works237,207