A Protocol for Development and Validation of a Diagnostic Model – The Hypertension Population Risk Tool (HTNPoRT) – to Predict Hypertension and Describe Risk Profiles: A Population-Based Cross-Sectional Study of Canadians
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
This is our protocol to develop and validate for Canadians, sex-specific diagnostic models which will predict hypertension and incorporate a diverse range of risk factors as predictors. The resulting risk profiles will enhance our understanding of the epidemiology of such risk factors in Canada. Canadian individuals can use the models and risk profiles to make informed decisions on how to reduce their risk of hypertension, while healthcare decision-makers can use them to identify populations at risk of hypertension and inform hypertension prevention strategies to reduce the burden of hypertension nationwide. These models and risk profiles would also help describe the relative importance of hypertension risk factors in Canada.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it