A virtual second opinion: Acceptability of a computer-based decision tool to assess older drivers with dementia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinicians face challenges in deciding which older patients with dementia to report to transportation administrators. This study used a qualitative thematic analysis to understand the utility and limitations of implementing a computer-based Driving in Dementia Decision Tool in clinical practice. Thirteen physicians and eight nurse practitioners participated in an interview to discuss their experience using the tool. While many participants felt the tool provided a useful 'virtual second opinion', specialist physicians felt that the tool did not add value to their clinical practice. Barriers to using the Driving in Dementia Decision Tool included lack of integration with electronic medical records and inability to capture certain contextual nuances. Opinions varied about the impact of the tool on the relationship of clinicians with patients and their families. The Driving in Dementia Decision Tool was judged most useful by nurse practitioners and least useful by specialist physicians. This work highlights the importance of tailoring knowledge translation interventions to particular practices.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it