ON-GOING ACTIVITIES OF THE NATIONAL TASK GROUP ON INTELLECTUAL DISABILITIES AND DEMENTIA PRACTICES
Bibliographic record
Abstract
The prevalence of diabetes in Canada and in the United States continues to increase and older adults are particularly at risk of developing this disease.In comparison to younger individuals, older adults are at an increased risk of complications, which can lead to functional impairment.Complications related to diabetes can affect the ability to safely drive a motor vehicle, and evidence suggests that drivers with diabetes are at an increased risk of collision (American Diabetes Association, 2012).The purpose of this presentation is to compare older drivers with diabetes and those without diabetes on a number of cognitive (e.g., Trail Making Test, Mini Mental State Exam), health (e.g., number of medications, medical comorbidities), and driving-related measures (e.g., situational avoidance, collisions).Data were extracted from the Candrive study, a large multisite prospective study of older drivers.At baseline, 115 of the 928 participants in the Candrive study (12%) had a diagnosis of Type I or Type II diabetes.Participants with and without a reported diagnosis of diabetes were compared on cognitive, visual perception, health, and driving-related variables.Older drivers with diabetes experience poorer overall health as demonstrated by multiple medical comorbidities and a greater number of medications.Older drivers with diabetes had poorer scores on the Trail Making Test -Part A and poorer scores on the Motor-Free Visual Perception Test.The findings will be discussed in terms of implications for healthcare professionals who interact with older adults with diabetes and make recommendations regarding fitness to drive.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".