The Landscape of Parkinson’s Disease Treatment in India: A National Cross-Sectional Survey of Clinical Practitioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
According to the Global Burden of Disease study, 5,75,946 people were living with Parkinson's Disease (PD) in India in 2016, constituting nearly 9.5% of the global PD population. 1 The relatively high proportion of early-onset PD (EOPD) in India adds to the socioeconomic burden and calls for focused public health strategies. 2 Prior studies have highlighted several critical challenges in PD care in India, such as suboptimal medication use, polypharmacy, anticholinergic burden and use of complementary and alternative medications. 3456 Studies also showed low uptake of advanced therapies, such as deep brain stimulation (DBS), due to financial constraints, lack of awareness, and late referrals. 7,8ite these insights, significant gaps remain in our understanding of the diversity of PD healthcare providers, access to allied health professionals, advanced therapies, and the impact of out-of-pocket expenditure on treatment adherence.Most existing evidence is drawn from patient charts or academic hospital-based samples, offering little insight into the perspectives of clinicians managing PD in diverse settings.To address these gaps, we conducted a nationwide cross-sectional survey of clinicians treating PD across India aiming to understand management practices, access to medications and advanced therapies such as DBS, availability of allied health professionals, funding sources, caregiving patterns, and challenges faced by healthcare providers treating PwPD.The survey questionnaire, developed by members of the Movement Disorders Society of India-National Parkinson Network (MDSI-NPN), comprised 50 questions covering six domains: clinical diagnosis, treatment availability and resources, funding availability, caregiving and burden of care, practice patterns, and miscellaneous topics in addition to 7 questions regarding participant demography.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it