MétaCan
Menu
Back to cohort
Record W2522762427 · doi:10.2337/dci16-0025

Response to Comment on Rickels et al. Intranasal Glucagon for Treatment of Insulin-Induced Hypoglycemia in Adults With Type 1 Diabetes: A Randomized Crossover Noninferiority Study. Diabetes Care 2016;39:264–270

2016· letter· en· W2522762427 on OpenAlexaff
Michael R. Rickels, Katrina J. Ruedy, Nicole C. Foster, Claude A. Piché, Hélène Dulude, Jennifer L. Sherr, William V. Tamborlane, Kathleen E. Bethin, Linda A. DiMeglio, R. Paul Wadwa, Andrew Ahmann, Michael J. Haller, Brandon M. Nathan, Santica M. Marcovina, Emmanouil Rampakakis, Linyan Meng, Roy W. Beck

Bibliographic record

VenueDiabetes Care · 2016
Typeletter
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsGDI Integrated Facility Services (Canada)
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineHypoglycemiaNasal administrationCrossover studyGlucagonType 2 diabetesDiabetes mellitusInsulinRandomized controlled trialIntensive care medicineInternal medicineEndocrinologyPharmacologyAlternative medicine

Abstract

fetched live from OpenAlex

Rickels et al. (1) report the results of a randomized crossover noninferiority study, making the firm assertion that intranasal glucagon was highly effective in treating insulin-induced hypoglycemia in type 1 diabetes. Unfortunately, the methods described in their article fail to support such a strong claim. We have several concerns about their ambitious conclusion. First, because of the controlled and artificial environment, this was not an effectiveness but an efficacy trial. Testing effectiveness requires carrying out the comparison in a “real-world setting” of severe hypoglycemia. Second, the design of the study does not seem appropriate considering that the intention was to prove intranasal glucagon was clinically equivalent to …

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.304
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueDiabetes CareSame topicDiabetes Management and ResearchFrench-language works237,207