MétaCan
Menu
Back to cohort
Record W4411291549 · doi:10.2337/db25-912-p

912-P: The Minimum Frequency of Well-Day Capillary Blood Ketone Testing Needed for Future Diabetic Ketoacidosis (DKA) Prediction in T1D

2025· article· en· W4411291549 on OpenAlexaboutno aff
Yucheng Zhang, Dalton Budhram, Priya Bapat, Sharon Dhaliwal, Cimon Song, Daniel Scarr, Abdulmohsen Bakhsh, Natasha J. Verhoeff, Alanna Weisman, Michael Fralick, NOAH IVERS, DAVID CHERNEY, George Tomlinson, DOUG MUMFORD, LEIF ERIK LOVBLOM, BRUCE A. PERKINS

Bibliographic record

VenueDiabetes · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetic ketoacidosisMedicineKetoacidosisDiabetes mellitusType 1 diabetesKetone bodiesPediatricsInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Introduction and Objective: Routine well-day surveillance of capillary ketone levels in T1D can predict future DKA risk, including in those using sodium glucose-linked transporter inhibitors (SGLTi). We assessed the impact of ketone testing frequency on prediction accuracy using data from the EASE trials. Methods: Data from 1685 participants assigned empagliflozin or placebo were analyzed. Ketone measurements were randomly selected to simulate less frequent testing than the protocol’s 2-3 tests/week. Maximum and other statistics of ketone levels were derived and categorized into 28-day intervals. Gradient Boosting Tree models were trained (80% of participants) and evaluated (remaining 20%) to predict DKA or severe ketosis in subsequent intervals. Areas Under the Receiver Operating Characteristic Curve (AUC) were compared using one-sided DeLong tests. Results: Out of the 11,218 intervals, 600 had DKA or severe ketosis events. Predictive performance decreased with less frequent testing: Biweekly compared to all data (2-3 tests per week) had slightly lower AUC (0.75 vs. 0.81, p = 0.081), but with testing at 21-day intervals, the AUC decreased significantly compared to biweekly (0.65, p = 0.027, Figure 1). Conclusion: To reasonably predict future ketoacidosis risk among T1D users or non-users of SGLTi, ketone testing on well-days every 2 weeks is the recommended strategy. Disclosure Y. Zhang: None. D.R. Budhram: None. P. Bapat: None. S. Dhaliwal: None. C. Song: None. D. Scarr: Employee; Medicenna Therapeutics. A.M.K. Bakhsh: Other Relationship; Sanofi. Advisory Panel; Ithanin. N. Verhoeff: None. A. Weisman: None. M. Fralick: None. N. Ivers: None. D. Cherney: Consultant; Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, AMGEN, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon, Inversago, GSK. Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring and Novo-Nordisk, Bayer. G.A. Tomlinson: None. D. Mumford: None. L. Lovblom: None. B.A. Perkins: Other Relationship; Abbott, Novo Nordisk, Sanofi. Advisory Panel; Abbott, Insulet Corporation, Sanofi, Novo Nordisk, Nephris, Vertex Pharmaceuticals Incorporated. Research Support; Novo Nordisk. Funding Diabetes Canada (OG-3-21-5572-BP)

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDiabetesSame topicDiet and metabolism studiesFrench-language works237,207