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Record W3006043206 · doi:10.2337/ds19-0016

Therapeutic Inertia in People With Type 2 Diabetes in Primary Care: A Challenge That Just Won’t Go Away

2020· article· en· W3006043206 on OpenAlexaff
Nemin Adam Zhu, Stewart B. Harris

Bibliographic record

VenueDiabetes Spectrum · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineGlycemicPrimary careType 2 diabetesDiabetes mellitusInertiaIntensive care medicineFamily medicine

Abstract

fetched live from OpenAlex

Therapeutic inertia is a prevalent problem in people with type 2 diabetes in primary care and affects clinical outcomes. It arises from a complex interplay of patient-, clinician-, and health system-related factors. Ultimately, clinical practice guidelines have not made an impact on improving glycemic targets over the past decade. A more proactive approach, including focusing on optimal combination agents for early glycemic durability, may reduce therapeutic inertia and improve clinical outcomes.

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)0.014
metaresearch head score (Gemma)0.046
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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0080.009
Open science0.0010.006
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.250
Teacher spread0.224 · 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

Citations28
Published2020
Admission routes1
Has abstractyes

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