Estimating homeostasis model assessment for insulin secretion by using multiple adaptive regression spline in healthy Taiwanese men
Bibliographic record
Abstract
The prevalence of type 2 diabetes (T2M) has been increasing drastically in recent two decades. One of the main underlying pathophysiology was decreased insulin secretion (ISEC). Even though there were many studies found the related factors affecting ISEC, no study used multiple adaptive regression spline (MARS) to build an equation estimating ISEC. In the present study, we used MARS to estimate hemostasis assessment model of β-cell (HOMA-β) in healthy Taiwanese men. Totally, there were 317 men enrolled. Participants who were taking medications related to metabolic syndrome were excluded. MARS was used to build an equation to estimate HOMA-β. Multiple linear regression (MLR) was taken as a bench mark for comparing the accuracy with MARS. The method with less estimation errors was considered to be more accurate. All the estimation errors were smaller for MARS. This indicated that MARS outperformed MLR. The equation built is shown below. The r 2 of this equation was 0.58. By using MARS, we built an equation which could accurately estimate HOMA-β in a healthy Taiwanese men cohort. The most important factor was HB, followed by TB, education level, sport area, GOT, GPT, and BF. This equation has a practical clinical use and could further explore which were the factors that were related to ISEC.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".