Development and Case-Control Validation of the Canadian Men’s Health Foundation’s Self Risk Assessment Tool:
Bibliographic record
Abstract
Background and Objective: To facilitate the engagement of men in 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 profile 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 disease 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 four Vancouver family practice clinics. Maximal correct classification proportions were determined and used as thresholds for categorization of risk to low, medium, or high categories. Results: For DM, sensitivity and specificity were 0.86 and 0.96 respectively and AUC was 0.88 (95% Confidence 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 first comprehensive men’s health self-risk assessment tool for seven important diseases. Moderate internal validity was demonstrated for five 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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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".