Uptake of telehealth in Parkinson's disease clinical care and research during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Traditionally, medical care and research in Parkinson's disease (PD) have been conducted with in-person encounters. The recent COVID-19 pandemic has profoundly impacted the delivery of in-person clinical care and clinical research. We conducted an online survey of active clinician members of the Parkinson Study Group (PSG) to evaluate the adoption of various non-face-to-face methods in clinical practice and research in PD during the COVID-19 pandemic. METHODS: We conducted a survey using the open-access online SurveyMonkey tool (http://www.surveymonkey.com). The survey had 27 items and was designed to elucidate clinical/research care before and during the COVID-19 pandemic. The survey was sent to 414 active PSG members with weekly reminders and it remained accessible for 30 days from May 2020. RESULTS: We received 142 responses, of which 133 (93.7%) provided demographic data. The clinical use of virtual visits via synchronous video conferencing increased from 39.5% pre-COVID-19 to 94.6% during the COVID-19 pandemic. Lack of access for patients (68.2%) and patient resistance (51.4%) were the top barriers for its use. Approximately 70% respondents stated that 75-100% of their research activities were suspended during the COVID-19 pandemic. Many sites had to fill out protocol deviations (38.2%), protocol exceptions (25.5%) or change their research profile due to layoffs (16.8%). The overall use of video conferencing increased from 30.3% to 64.1%. CONCLUSION: The current results suggest a need for flexibility in conducting office visits and clinical trials in PD patients. Technology has the potential to enhance patient care and convenience, when in-person visits can be challenging.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".