"Next Generation" Approaches in Diet Pattern Analysis: Assessing the Impact of Different Statistical Methods and Physiological Intermediate Variables
Bibliographic record
Abstract
The objective of this thesis was to compare dietary patterns (DPs) derived using principal component analysis (PCA), reduced rank regression (RRR) and partial least square (PLS) analysis as well as to evaluate how the selection of disease-related intermediary variables (DRIVs) alters the DPs produced. The association of DPs with type 2 diabetes mellitus (T2DM) was also explored across different statistical techniques and DRIVs. All analyses were conducted using the Insulin Resistance Atherosclerosis Study (IRAS), a cohort consisting of middle-aged adults initially free of T2DM. PCA, PLS and RRR and each a priori defined DRIVs (inflammatory biomarkers) each yielded distinctive sets of DPS; however, there were no significant differences in how the different methods or DRIVs predicted T2DM. This could be due to the limited number of T2DM converters, the duration of follow-up, the characteristics of the FFQ and/or the population under investigation.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".