Driving Cessation Risk Tool (DriveCRT): study protocol for a predictive algorithm assessing the 6-year risk of driving cessation in older adults
Bibliographic record
Abstract
Introduction: Population aging is occurring in nearly all countries, with adults aged 80 and older representing the fastest-growing segment. Millions of older adults will stop or limit their driving after a lifetime of relying on it for independence and mobility. Driving cessation in older adults is linked to increased depressive symptoms, reduced physical functioning, and diminished social health. Anticipating driving cessation will be increasingly important as our population ages. Existing algorithms are not often operationalized in a way that enables older adults to use on their own because they require inputs from clinical tests. Methods and analysis: The objective of this study is to develop and validate algorithms with and without clinical measures to predict the risk of driving cessation in 6 years in adults aged 65 years and older. The study cohort will be derived using both the Comprehensive and Tracking cohorts in the Canadian Longitudinal Study on Aging (CLSA) among adults aged 65 years and older and driving at baseline (2010–2015; 11,762 drivers). Cases will be identified based on the incidence of driving cessation at 6 years following baseline measurement (2018–2021; 948 individuals stopped driving). Prespecified predictors include sociodemographic, self-reported health, functional and health condition variables. The base model will use self-reported information only, while the extended model will include additional clinical measures. Expected outcomes include validated multivariable models with measures of calibration and discrimination to assess predictive performance. The better performing algorithm will be used to support early identification of individuals at risk of driving cessation, enabling timely interventions and planning to maintain independence and mobility. This study protocol and the reporting of model estimation results will be guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statements.
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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".