Reproducibility of impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) classification: a systematic review
Bibliographic record
Abstract
BACKGROUND: The classifications of impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) represent glucose levels above normal, but below the decision threshold for diabetes. We sought to determine what the reproducibility of these classifications was when repeat tests were performed by conducting a systematic review of the literature. METHODS: All primary studies published in English of any study design were included. Studies were excluded if they did not follow the World Health Organization or American Diabetes Association diagnostic criteria, used whole blood as the specimen type, a glucose meter for analysis, or performed repeat testing greater than 8 weeks apart. RESULTS: Five papers had reproducibility data for IGT or IFG, two of which where from the same population but sampled differently. The kappa coefficients, indicating agreement between repeat tests that exceeded chance, indicated poor to fair agreement for IGT (0.04, 0.22, 0.38, 0.42) and moderate agreement for IFG (0.44 and 0.56). Similarly, the observed reproducibility was slightly lower for IGT (33%, 44%, 47%, 48%) compared to IFG (51%, 64%). In two studies for which data were available for both IGT and IFG, the average reproducibility was lower (49%) for the prediabetes group compared to the diabetes group (73%) or the normal group (93%). CONCLUSIONS: Poor reproducibility of IGT and IFG classification suggests caution should be exercised when interpreting a single test result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".