The past, present, and future of telemedicine for Parkinson's disease
Bibliographic record
Abstract
Travel distance, growing disability, and uneven distribution of doctors limit access to care for most Parkinson's disease (PD) patients worldwide. Telemedicine, the use of telecommunications technology to deliver care at a distance, can help overcome these barriers. In this report, we describe the past, present, and likely future applications of telemedicine to PD. Historically, telemedicine has relied on expensive equipment to connect single patients to a specialist in pilot programs in wealthy nations. As the cost of video conferencing has plummeted, these efforts have expanded in scale and scope, now reaching larger parts of the world and extending the focus from care to training of remote providers. Policy, especially limited reimbursement, currently hinders the growth and adoption of these new care models. As these policies change and technology advances and spreads, the following will likely develop: integrated care networks that connect patients to a wide range of providers; education programs that support patients and health care providers; and new research applications that include remote monitoring and remote visits. Together, these developments will enable more individuals with PD to connect to care, increase access to expertise for patients and providers, and allow more-extensive, less-expensive participation in research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".