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Record W1545852081 · doi:10.25011/cim.v37i6.22247

Emerging Strategies for the Preservation of Pancreatic Beta-cell Function in early Type 2 Diabetes

2014· article· en· W1545852081 on OpenAlexafffundvenue
Ravi Retnakaran

Bibliographic record

VenueClinical and investigative medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationHeart and Stroke Foundation of Canada
KeywordsType 2 diabetesBETA (programming language)Context (archaeology)Beta cellDiabetes mellitusMedicineInsulinFunction (biology)CellInternal medicineEndocrinologyBiologyCell biologyComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

A fundamental problem in the clinical management of type 2 diabetes is the inability to prevent the ongoing deterioration of pancreatic beta-cell function over time that underlies the chronic progressive nature of this condition. Importantly, beta-cell dysfunction has both reversible and irreversible components. Furthermore, the amelioration of reversible beta-cell dysfunction through the early institution of short-term insulin-based therapy has emerged as a strategy that can yield temporary remission of type 2 diabetes. In this context, we have forwarded a novel therapeutic paradigm consisting of initial induction therapy to improve beta-cell function early in the course of diabetes followed by maintenance therapy aimed at preserving this beneficial beta-cell effect. Ultimately, this approach may yield an optimized therapeutic strategy for the durable preservation of beta-cell function and consequent modification of the natural history of type 2 diabetes.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.128
GPT teacher head0.333
Teacher spread0.204 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations5
Published2014
Admission routes3
Has abstractyes

Explore more

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