Papers that Might Change Practice - Review of "The Introduction of a New Screening Tool for the Identification of Cognitively Impaired Medically At-Risk Drivers
Bibliographic record
Abstract
WHY THIS PAPERFamily physicians, geriatricians, and others need an effective, efficient, office-based approach to assessing driving risk in older patients.The Screen for the Identification of cognitively impaired Medically At-Risk Drivers (SIMARD) has been suggested on the web (http://www.mard.ualberta.ca/Home/ SIMARD/), in a Pfizer-sponsored toolkit for physicians, and in other venues as a way to do this.The 2010 BC Guide in Determining Fitness to Drive recommends using the SIMARD to determine whether patients with persistent cognitive impairment and a long list of additional conditions (i.e., chronic renal disease, end-stage renal disease, or renal transplant; chronic obstructive pulmonary disease or other respiratory disease; vestibular disorders; congestive heart failure or postcardiac arrest; mood disorders, attention-deficit hyperactivity disorder or schizophrenia; stroke or cerebral aneurysm; multiple sclerosis, Parkinson's disease, or cerebral palsy; traumatic brain injury; intracranial tumors; obstructive sleep apnea-hypopnea syndrome or narcolepsy; psychotropic drug use; general debility or lack of stamina) should be referred to DriveABLE for a driving assessment.(1) What is the available evidence on the SIMARD?Does it justify such widespread use?To answer this question, we reviewed the only published paper then available about this instrument.
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 | 0.057 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".