Teleneuropsychology for vascular cognitive impairment: Which tools do we have?
Bibliographic record
Abstract
The halt of clinical activities imposed during the COVID-19 pandemic forced clinicians to find alternative strategies to provide continuity of care and services, and led to a renewed interest in use of teleneuropsychology (TNP) to remotely assess patients. Recent TNP guidelines recommend maximizing the reproduction of standard in-person assessment, particularly through videoconferences. However, consistency of the adaptations of usual cognitive tests to videoconference needs further elucidation. This review aims at critical reviewing which cognitive tests could be recommended for a remote evaluation of patients with vascular cognitive impairment (VCI) among those widely recognized as reference standards. Current evidence supports the use of global cognitive efficiency (MMSE and MoCA), verbal memory (Revised Hopkins Verbal Learning Test), and language tests (phonemic and semantic verbal fluencies, Boston Naming Test), while there is a lack of strong validity support for measures of visuospatial functions (Rey-Osterreith Complex Figure), and executive functioning and processing speed (Trail making Test, and Digit symbol or Symbol digit tests). This represents a major limitation in the evaluation of VCI because its cognitive profile in often characterized by attention and executive deficits. At present, a videoconference TNP visit appears useful for a brief evaluation of global cognitive efficiency, and to 'triage' patients towards a second level in person evaluation. In future, hybrid models of TNP based on data collected across multiple modalities, incorporating both adaptation of usual cognitive tools and new computerized tools in the supervised videoconference setting, are likely to become the best option for a comprehensive remote cognitive assessment.
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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".