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Record W2406649600 · doi:10.4239/wjd.v6.i17.1323

Skeletal muscle as a therapeutic target for delaying type 1 diabetic complications

2015· review· en· W2406649600 on OpenAlex
Samantha K. Coleman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Journal of Diabetes · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSkeletal muscleDiabetes mellitusInsulinDiseaseType 1 diabetesBioinformaticsType 2 diabetesInternal medicineEndocrinologyIntensive care medicine

Abstract

fetched live from OpenAlex

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease targeting the pancreatic beta-cells and rendering the person hypoinsulinemic and hyperglycemic. Despite exogenous insulin therapy, individuals with T1DM will invariably develop long-term complications such as blindness, kidney failure and cardiovascular disease. Though often overlooked, skeletal muscle is also adversely affected in T1DM, with both physical and metabolic derangements reported. As the largest metabolic organ in the body, impairments to skeletal muscle health in T1DM would impact insulin sensitivity, glucose/lipid disposal and basal metabolic rate and thus affect the ability of persons with T1DM to manage their disease. In this review, we discuss the impact of T1DM on skeletal muscle health with a particular focus on the proposed mechanisms involved. We then identify and discuss established and potential adjuvant therapies which, in association with insulin therapy, would improve the health of skeletal muscle in those with T1DM and thereby improve disease management- ultimately delaying the onset and severity of other long-term diabetic complications.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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