Spinal muscular atrophy in India: Patient journey, access to care, treatment barriers, and strategic recommendations: Insights from experts
Bibliographic record
Abstract
INTRODUCTION: Spinal muscular atrophy (SMA) is a neuromuscular disease that affects patients and caregivers worldwide, including in India, with a significant economic burden. METHODS: A comprehensive literature review was conducted to evaluate SMA, patient journeys, healthcare access, and treatment barriers in India, forming the basis for an expert panel discussion. RESULTS: The experts highlighted that early detection of SMA through newborn, carrier, or prenatal screening, and diagnosis through genetic testing enable timely interventions including disease-modifying therapies (DMTs) and multidisciplinary care. Currently, in India, Risdiplam is the only Drugs Controller General of India (DCGI) approved DMT for SMA. It is administered orally and approved for use in SMA type 1, 2, 3, and 4 among pediatric and adult patients. The management of SMA often revolves around the management of its complications, which requires respiratory, nutritional, and orthopedic care, both while awaiting and after receiving DMTs. Therefore, the gold standard for SMA care requires the availability of multidisciplinary care involving specialists from various fields. Despite the availability of DMTs, challenges such as affordability and timely access to these therapies remain major hurdles faced by SMA patients in India. Additionally, a lack of awareness among healthcare professionals, contributed to underdiagnosis and undiagnosed cases, further exacerbating the situation. CONCLUSION: This review provides insights from the expert opinions of Indian pediatricians, neurologists, geneticists, pediatric neurologists, and pediatric pulmonologists on the burden of SMA, diagnosis and management practices, importance of a multidisciplinary approach, challenges faced in SMA care in India, and strategies to overcome these challenges.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".