Prescribing Pattern for Parkinson’s Disease in Indian Community before Referral to Tertiary Center
Bibliographic record
Abstract
BACKGROUND: Several factors determine the choice of medications in patients with Parkinson's disease (PD). We aimed to analyze the pattern of prescription of drugs in patients with PD before attending a tertiary-care center. METHODS: The study included chart review of 800 PD patients attending the Department of Neurology of the National Institute of Mental Health and Neurosciences in Bangalore, India. RESULTS: The mean age at onset was 51.1±11.8 years. The mean duration of illness was 41.7±43.6 months. At first visit, 79.4% (group 1, n=635) of patients were on medications, 10% (group 2, n=80) were on medications but later discontinued, and 10.6% (group 3, n=85) were drug-naïve. Overall, levodopa was prescribed in 94.8%, trihexyphenidyl in 40.4%, dopamine agonists in 23.2%, and amantadine in 17.2% either as monotherapy or in combination. In group 1, 37.8% were on monotherapy, with levodopa being the most commonly used agent (33.1%), followed by trihexyphenidyl (2.2%), dopamine agonists (1.6%), and amantadine (0.6%). Among those on polytherapy, levodopa plus trihexyphenidyl was the preferred combination (23.9%). In group 2, levodopa monotherapy was also most common (72.5%), followed by trihexyphenidyl monotherapy (7.5%). CONCLUSIONS: Levodopa and trihexyphenidyl were the most commonly prescribed drugs in our patients. A higher use of trihexyphenidyl could be due to its easy availability, low cost, and better tolerability in our patients, who were relatively young at the time of onset of their disease. The choice of antiparkinsonian medications at the primary and secondary care levels in India may be inappropriate, and newer guidelines tailored to the Indian context are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".