A Comparison of Principal Component-Based and Multivariate Regression of Cardiac Disease
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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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".