Red flags for remote cognitive assessment: An expert consensus study using the Delphi method on behalf of the Canadian Consortium on Neurodegeneration in Aging
Bibliographic record
Abstract
Abstract Remote cognitive diagnostic assessment offers numerous benefits, including increased access to care, but it may not always be appropriate, and guidelines are lacking. Our goal was to develop a clinical tool to determine a patient’s suitability for undergoing remote cognitive assessment. A multidisciplinary workgroup, composed of experts in remote assessment, was convened under the auspices of the Canadian Consortium on Neurodegeneration in Aging. Delphi, an anonymous group consensus method, was used to determine ‘red flags’ for remote cognitive diagnostic assessment. The process consisted of one round of flag generation, then two rounds of flag scoring based on effectiveness, reproducibility, and efficiency. In the first round, 11 respondents generated 67 potential flags. In subsequent rounds, 8 and 9 respondents, respectively, scored the flags, yielding 14 red flags that met the predetermined consensus criteria. This research led to the creation of a novel clinical decision-making infographic to support multidisciplinary clinicians in determining a patient’s readiness to undergo remote cognitive and behavioral diagnostic assessment. Highlights A timely and accessible diagnosis of dementia is crucial for optimal patient care. Although clinicians are increasingly using telemedicine, guidelines on patient suitability for remote cognitive and behavioral assessment are lacking. To address this knowledge gap, we developed a clinical decision-making tool to determine if a cognitive evaluation via telemedicine should be avoided. To synthesize expert opinion, we used the Delphi method, an anonymous group consensus method that reduces eminence bias. In collaboration with knowledge translation experts, an infographic describing the final 14 red flags for remote cognitive assessment was developed for clinicians to determine the appropriateness of patients for remote dementia diagnostic assessment. Research in context Systematic Review PubMed and Google Scholar were used to survey the literature. Our review found that although there was a need for better access to dementia care, a framework to provide such care remotely is in development. We identified a gap in clinical guidelines on contraindications for remote cognitive assessment. Interpretation This study created a clinical decision-making tool as an accessible infographic for clinicians to use when considering a remote cognitive diagnostic assessment. This guideline is based on expert consensus. Future Directions In future studies, patient and caregiver perspectives should be incorporated into the decision-making process, and this tool should be validated in clinical contexts. Some of the identified flags for remote assessment are modifiable, and strategies to mitigate burden on patients and caregivers are warranted.
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How this classification was reachedexpand
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".