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Record W2304053389 · doi:10.2337/db15-1640

Comment on Kolwicz et al. Enhancing Cardiac Triacylglycerol Metabolism Improves Recovery From Ischemic Stress. Diabetes 2015;64:2817–2827

2016· letter· en· W2304053389 on OpenAlexaff
Michael Zaugg, Eliana Lucchinetti, Alexander S. Clanachan

Bibliographic record

VenueDiabetes · 2016
Typeletter
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiabetes mellitusMedicineCardiologyInternal medicineType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

Kolwicz et al. (1) presented an interesting and elegantly conducted study where they examined the role of diacylglycerol transferase 1 (DGAT1) overexpression in cardiac triacylglycerol turnover rates under ischemia-reperfusion conditions. Mice with myosin heavy-chain–mediated cardiac-specific overexpression of DGAT1 exhibited elevated triacylglycerol contents and ischemia-induced lipolysis. Furthermore, enhanced triacylglycerol turnover was associated with improved functional recovery during reperfusion. The authors concluded that elevated triacylglycerol content and turnover provide protection from ischemia-reperfusion injury in nondiabetic hearts. In the discussion of their results, …

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0240.026
Insufficient payload (model declined to judge)0.0050.008

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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