Development and validation of a population-based risk algorithm for premature mortality in Canada: the Premature Mortality Population Risk Tool (PreMPoRT)
Bibliographic record
Abstract
Introduction: To develop and validate the Premature Mortality Population Risk Tool (PreMPoRT), a population-based risk algorithm that predicts the 5-year incidence of premature mortality among the Canadian adult population. Methods: Retrospective cohort analysis used six cycles of the Canadian Community Health Survey linked to the Canadian Vital Statistics Database (2000-2017). The cohort comprised 500 870 adults (18-74 years). Predictors included sociodemographic factors, self-perceived measures, health behaviours and chronic conditions. Three models (minimal, primary and full) were developed. PreMPoRT was internally validated using a split set approach and externally validated across three hold-out cycles. Performance was assessed based on predictive accuracy, discrimination and calibration. Results: The cohort included 267 460 females and 233 410 males. Premature deaths occurred in 1.40% of females and 2.05% of males. Primary models had 12 predictors (females) and 13 predictors (males). Shared predictors included age, income quintile, education, self-perceived health, smoking, emphysema/chronic obstructive pulmonary disease, heart disease, diabetes, cancer and stroke. Male-specific predictors were marital status, Alzheimer's disease and arthritis while female-specific predictors were body mass index and physical activity. External validation cohort differed slightly in demographics. Female model performance: split set (c-statistic: 0.852), external (c-statistic: 0.856). Male model performance: split set and external (c-statistic: 0.846). Calibration showed slight overprediction for high-risk individuals and good calibration in key subgroups. Conclusions: PreMPoRT achieved the strongest discrimination and calibration among existing prediction models for premature mortality. The model produces reliable estimates of future incidence of premature mortality and may be used to identify subgroups who may benefit from public health interventions.
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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 | 0.009 | 0.030 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".