A Comparison of Principal Component-Based and Multivariate Regression of Cardiac Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Selecting factors suitable to use in a regression model is often a complicated process: the researcher strives to retain all theoretically important factors while avoiding high correlations among independent variables. This chapter models cardiac disease and compares the explanatory ability of component-based multivariate regression models, created through the use of principal component analysis (PCA), with that of direct variable-based, multivariate regression models. The variable-based demographic and socio-economic model contains education, sex, and 3 age factors; in contrast, the component-based model contains age as well as several modifiable risk factors: education, income, family, and housing factors. Moreover, the latter model also has statistically higher explanatory power. Components made through data reduction techniques may not always be interpretable, but, given closer examination of individual components, a component-based model becomes more interpretable. Further, all important factors will potentially be present in models. As such, component-based modelling can be a useful tool for research and public health planning. A key limitation of this work, to be addressed in future research, is the use of a variable (cardiac catheterisation procedures) that remains a crude proxy for cardiovascular disease. More effective analysis will be performed as data becomes available. Exploration into the relationship of factor and their spatial patterns will also be considered.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it