Bold policy changes are needed to meet the need for organ transplantation in India
Bibliographic record
Abstract
Twenty-five years after India passed legislation to legalize brain death, deceased donor transplantation remains underdeveloped while the country has established formidable capacity for living donor transplantation. Because of a large number of potential deceased donors, there is hope that deceased donation could help meet India's enormous need for organ transplantation. However, significant policy and practical barriers limit progress. The vast majority of potential deceased donors are poor motor vehicle accident victims who present for care in hospitals without the necessary infrastructure or expertise to support deceased donation. In contrast, transplant infrastructure and expertise are concentrated in private hospitals and are only accessible to those with the ability to pay. Given these realities, the potential of deceased donor transplantation can only be recognized if Indians who are likely to donate organs are also provided access to transplantation. In this viewpoint, we review the current status of organ transplantation in India and propose new policies to establish a national organization to oversee deceased donor services in all states, to fund resources needed to support deceased donation, to leverage the existing living donor infrastructure to advance deceased donor transplantation, and call for establishment of government policy on funding for posttransplant care and immunosuppression.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".