DEVELOPMENT AND CASE-CONTROL VALIDATION OF THE CANADIAN MEN’S HEALTH FOUNDATION’S SELF RISK-ASSESSMENT TOOL: “YOU CHECK”
Bibliographic record
Abstract
Background and Objective: To facilitate the engagement of men in the evaluation of their own health status and risk of disease, we have developed and validated the Canadian Men’s Health Foundation’s self-risk assessment tool (“You Check”). In a single questionnaire, the “You Check” tool estimates the 10-year risk for myocardial infarction (MI), diabetes type 2 (DM), osteoporosis (OS), erectile dysfunction (ED), and low testosterone (LT). Additionally, the tool provides the user with his risk-factor profi le for prostate cancer and his current risk of depression (using the Center for Epidemiologic Studies Depression scale). Materials and Methods: Known risk factors for each disease were collated, the questionnaire designed, and risk scores for each dis-ease were assigned by clinical experts. A risk formula was developed using the sum of risk scores divided by their own range. We validated the risk models with case-control data from a retrospective review of 400 outpatient records from 4 Vancouver family practice clinics. Maximal correct classifi cation proportions were determined and used as thresholds for categorization of risk to low, medium, or high categories. Results: For DM, sensitivity and specifi city were 0.86 and 0.96 respectively and the Area Under Curve was 0.88 (95% Confi dence Interval [CI] 0.81-0.94). For MI these values were 0.70 and 0.93, and 0.75 (0.65-0.85); for LT 0.70 and 0.90 and 0.75 (0.66–0.84); for OS 0.70 and 0.86 and 0.70 (0.61–0.80); and for ED 0.42 and 0.96 and 0.66 (0.58–0.75). Conclusion: This is the fi rst comprehensive men’s health self-risk assessment tool for 7 important diseases. Moderate internal validity was demonstrated for 5 diseases, meeting the public health objectives of “You Check” which is now in the public domain and under appropriate monitoring and evaluation (https://youcheck.ca).
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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 | 0.050 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".