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Record W2142628001 · doi:10.2337/dc14-1636

Response to Comment on Kramer et al. Glucagon Response to Oral Glucose Challenge in Type 1 Diabetes: Lack of Impact of Euglycemia. Diabetes Care 2014;37:1076–1082

2014· letter· en· W2142628001 on OpenAlexaff
Caroline K. Kramer, Ravi Retnakaran, Bernard Zinman

Bibliographic record

VenueDiabetes Care · 2014
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineGlucagonInternal medicineDiabetes mellitusEndocrinologyInsulin responseType 2 diabetesInsulinPlasma glucose

Abstract

fetched live from OpenAlex

We thank Ali et al. (1) for their comments on our article (2). In this study, we evaluated the impact of euglycemia versus hyperglycemia on the glucagon response to an oral glucose tolerance test (OGTT) in 10 participants with type 1 diabetes and showed that these patients have a paradoxical increase in glucagon in response to oral glucose that is not reversed when euglycemia is achieved prior to the test. This conclusion was based on assessment of glucagon response to the OGTT by five diverse statistical approaches, of which only one suggested a differential response under …

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)0.021
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 score0.029
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0290.023
Insufficient payload (model declined to judge)0.0060.008

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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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