Modified Quantitative Insulin Sensitivity Check Index Is Better Correlated to Hyperinsulinemic Glucose Clamp than Other Fasting-Based Index of Insulin Sensitivity in Different Insulin-Resistant States
Bibliographic record
Abstract
Fasting-based index estimates of insulin sensitivity were compared with euglycemic hyperinsulinemic clamp (IS clamp) measurements in 148 subjects: normal controls (n = 46), and obese (n = 12), polycystic ovary syndrome (n = 16), first-degree relatives of type 2 diabetic (n = 17), impaired glucose tolerance (n = 28), and type 2 diabetic (n = 29) patients. The fasting-based indexes tested included log homeostasis model assessment (HOMA), the quantitative insulin sensitivity check index (QUICKI), the revised QUICKI, and a new revised QUICKI using fasting plasma glycerol. In the population studied, at 40 mU/m(2).min (n = 30) revised QUICKI (r = 0.86; P < 0.0001) and QUICKI-glycerol (r = 0.87; P < 0.0001) gave higher correlations with the IS clamp than QUICKI and log HOMA (r = 0.78 and r = -0.78; P < 0.001). For subjects tested at 75 mU/m(2).min (n = 118), comparable correlations were found for all indexes (r > 0.80; P < 0.0001). When studied in subgroups, revised QUICKI and QUICKI-glycerol give significantly higher correlations with the IS clamp than other indexes for lean control subjects studied at 40mU/m(2).min and impaired glucose tolerance subjects. We confirmed, in a large patient population with a wide range of insulin sensitivities, that no single test is superior in all groups of patients. However, QUICKI and revised QUICKI are good indexes that offer correlations similar to or higher than values obtained with log HOMA. Such indexes are simple tools to estimate insulin sensitivity appropriate for epidemiological studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".