Maximizing Fidelity of Neuropsychology Assessments in Fully Remote Studies (Preprint)
Bibliographic record
Abstract
BACKGROUND Remote, interdisciplinary, observational clinical studies and clinical trials are increasingly emerging in the scientific literature. Remote videoconference-based neuropsychological assessment offers numerous advantages, including improved access to care, reduced travel burden, and the ability to monitor patients over time. However, such methodologies present challenges, particularly regarding the fidelity of collected data. Data fidelity, defined as the accuracy, completeness, and consistency of data, can be compromised by technical issues, variability in testing environments, and risks of data loss. These challenges necessitate the development of robust, standardized protocols to ensure high-quality data collection and analysis. OBJECTIVE This study aimed (1) to evaluate the data fidelity of a remote videoconference-administered neuropsychology protocol in the context of the Health in Aging, Neurodegenerative Diseases, and Dementias in Ontario (HANDDS-ONT) study, guided by the Ontario Neurodegenerative Disease Research Initiative (ONDRI); (2) to generate three roadmaps to support data fidelity procedures for future remote neuropsychological research; and (3) to characterize the sample and their neuropsychological assessment performance. Quality assurance and quality control procedures were implemented, and missing data, virtual environment-related errors, and outcomes of quality assurance and quality control measures were evaluated to assess data fidelity. METHODS 148 participants (62% female; median age=67, mean=15.1 education years) completed the neuropsychology protocol. Data were analyzed as descriptive statistics. RESULTS Implementing our quality assurance and control procedures, the average number of queries per participant was 5.59 during the data monitoring phase and 0.30 during the cleaning and curation pipeline phase, with only 0.34% of data missing. Virtual environment factors, such as internet connectivity and distractions, had minimal impact on data quality as only 8 participants (5.4%) had 1-2 tasks impacted by the virtual environment resulting in missing data. The fidelity of the remote videoconference-administered neuropsychology protocol was comparable to that of in-person assessments, and our procedures effectively minimized missing data and reduced the need for data corrections. The study highlighted the feasibility of collecting reliable neuropsychology data remotely while identifying practical adaptations to mitigate potential challenges. CONCLUSIONS The current study highlights the potential of remote videoconference-administered neuropsychological assessment to deliver high-fidelity data in clinical and research settings. The protocol performed similarly to an in-person neuropsychology protocol as it pertained to quality control indices (i.e., missingness and number of data corrections needed), and few virtual environment-related factors impacted data missingness. Quality assurance and quality control measures were crucial for ensuring the data were collected robustly remotely. The proposed roadmaps offer a template for future studies, addressing common challenges in remote neuropsychological testing and enabling wider adoption of telehealth methodologies. By advancing standardized protocols, this research supports the ongoing evolution of remote, interdisciplinary approaches to studying and managing neurodegenerative diseases. CLINICALTRIAL CTO Project ID #3589
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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 | 0.156 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".