The virtual interface for neurodegenerative disease management: A systematic literature review on the feasibility of telemedicine in dementia care
Bibliographic record
Abstract
Abstract Background As the global population ages and the prevalence of dementia rises, telemedicine has garnered attention as a means of delivering care to a frail cognitively‐impaired population through a virtual interface. The benefits of telemedicine‐based in person patient‐physician encounters include increased access to healthcare irrespective of distance, as well as reduction in travel costs and carbon emissions. While Telemedicine has proven feasible and effective in other areas of medicine, its use in a cognitively‐impaired population is less well studied and poses unique challenges. The current literature review aims to compare and contrast the use of televisits across neurodegenerative disease, with a particular focus on dementia care. Method We performed a systematic review of the literature published between January 2000 and January 2020. Databases OVID Medline and PubMed were searched using keywords “Telemedicine”, “Neurodegenerative disease”, “Dementia” and their respective synonyms. Eligible articles were selected after screening the titles and abstracts. Studies were included if they examined the use of virtual healthcare visits (i.e. televisits) in the management of patients with neurodegenerative diseases. Result The search yielded 503 potentially eligible articles. After further screening, a total of 35 articles met inclusion criteria. Positive benefits of televisits across neurodegenerative diseases included high rates of patient satisfaction, feasibility and validity. A particular challenge that emerged as unique in dementia care is a lack of interpersonal engagement. Conclusion While telemedicine has clear potential benefits for neurodegenerative disease management, further research is required to assess long‐term clinical and quality of life outcomes in patients with dementia.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".