Clinical translation of neuro-regenerative medicine in India: A study on barriers and enabling strategies
Bibliographic record
Abstract
We present the findings of a study of barriers and enabling strategies to clinical translation of Neuro-Regenerative Medicine (Neuro-RM) technologies in India. Twenty-three people were included in this qualitative study, including researchers, clinicians, firm representatives and policy makers working in Neuro-RM. The study has identified barriers that may arise at each stage of translation and how these are being addressed. Understanding of the molecular and cellular basis of Neuro-RM is being supported through government investment in existing neuroscience centres and the creation of new centres with regenerative medicine expertise. Clinical trials benefit from the support of clinicians who partner with researchers in study design and data collection. Government agencies have developed guidelines to inform best practices in preclinical and clinical studies. Addressing the barriers to Neuro-RM translation identified in this study can be achieved through continued support for capacity building and priority setting in preclinical studies, international efforts to achieve clinical trial protocol standardization, and multidisciplinary collaborations between clinicians, researchers, government and industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".