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Record W6977304822 · doi:10.6084/m9.figshare.29246826

Time to replace the oral glucose tolerance test for cystic fibrosis related diabetes first-step screening? Establishing glycemic tools relevant to cystic fibrosis

2025· article· en· W6977304822 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsCystic fibrosis-related diabetesCystic fibrosisFructosamineImpaired glucose toleranceBody mass indexDiabetes mellitusGlucose tolerance testGlycemic

Abstract

fetched live from OpenAlex

As the life expectancy of people with cystic fibrosis (CF) increases, complications related to CF, such as CF-related diabetes (CFRD), are of great concern. Oral glucose tolerance test (OGTT) is the current gold standard test to screen for CFRD, which is associated with reduced lung function and body mass index (BMI). However, this is a cumbersome test with poor adherence, and emerging evidence suggests that HbA1c or serum fructosamine might be viable alternative screening tools. A multi-center study across four Canadian adult CF centers was conducted to determine the ability of HbA1c and serum fructosamine levels to predict screening OGTT results. Cross-sectional outcome data, including ppFEV1 and BMI within two months of testing, were collected. A total of 183 CFRD screening encounters over five years were included. HbA1c and the fructosamine-to-albumin ratio had similar predictive performances for CFRD as determined by OGTT-defined cutoffs (AUC both 0.68) and for impaired glucose tolerance (AUC 0.69 and 0.64, respectively). However, the specificity of FAR is lower, meaning fewer OGTTs can be avoided if FAR is used as a first-step screening test when screening for either CFRD and/or IGT compared to HbA1. The optimal HbA1c cut-off for CFRD screening was ≥5.5% (sensitivity, 95%; specificity, 32%). Regression analyses demonstrated a strong inverse correlation between HbA1c and ppFEV1 (p < 0.0001), while the OGTT was inversely correlated with ppFEV1 (p < 0.05), and the fructosamine-to-albumin ratio was inversely correlated with BMI (–0.9; 95% CI −1.5, −0.4; p = 0.002), but not with ppFEV1 within 2 months of testing. HbA1c is validated as a first step in screening for CFRD, allowing one-third of the patients to avoid the OGTT. As HbA1c demonstrated a stronger correlation with ppFEV1 than the OGTT, consideration could be made to redefine CFRD based on HbA1c. What is already known on this topic—The current gold standard for screening for CFRD is the oral glucose tolerance test (OGTT). However, the test has poor patient adherence; recently, alternative approaches are suggested.What this study adds – This study suggests that screening for CFRD with an HbA1c test may be a viable first step in reducing the OGTT burden for patients.How this study might affect research, practice or policy: This study provides strong evidence for the use of HbA1c as a first step in the screening algorithm for CFRD, reducing the need for OGTT. What is already known on this topic—The current gold standard for screening for CFRD is the oral glucose tolerance test (OGTT). However, the test has poor patient adherence; recently, alternative approaches are suggested. What this study adds – This study suggests that screening for CFRD with an HbA1c test may be a viable first step in reducing the OGTT burden for patients. How this study might affect research, practice or policy: This study provides strong evidence for the use of HbA1c as a first step in the screening algorithm for CFRD, reducing the need for OGTT.

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)0.025
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 score0.187
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

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