Multivariate Relationships Between Physiologic and Anthropometric Variables: A Data Based Analysis
Bibliographic record
Abstract
To establish the relationship between two sets of variables measured on the same subject, canonical correlation analysis (CCA) is the most appropriate and popular method. In this study we consider two sets of variables which consist of different types of measurements. Here one set has three physiologic variables whereas the other set has eighteen anthropometric variables (mentioned in section 3.1 with abbreviations). The aim of this study is to evaluate the relationship between two sets and to find out the factors which influence the relationship between the two sets. This study has revealed that first two canonical correlations were significant and WT, APC, TVC, CCN, MUAC and WC (anthropometric variables) are the risk factors for SBP and DBP (physiologic variables). Furthermore considering these risk factors, General Linear Model (GLM) indicated that CCN and WC are highly significant factors which influence the physiologic set. Thus the model (CCA+GLM) provide the most important factors which influence physiologic variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".