Bibliographic record
Abstract
Introduction In an update to previous work, David Matas, David Kilgour and Ethan Gutmann in June 2016 estimated that China is transplanting between 60,000 to 100,000 organs a year. The estimate comes from adding up transplant volumes from individual hospitals in China. Transplantation volume of this order raises a question of the sourcing of organs. The trio have identified prisoners of conscience killed for their organs as the chief source - Tibetans, Uighurs, House Christians and primarily practitioners of the spiritually based set of exercises Falun Gong. The Government of China has denied the Matas-Kilgour-Gutmann conclusion of transplant volume, pointing to statistics about use of anti-rejection drugs. The purpose of this presentation would be to assess this particular Government of China response by a focus on use of anti-rejection drug statistics as an indicator of transplant volume in China. Materials and Methods The presentation would give a brief summary of the Matas-Kilgour-Gutmann update and bring its analysis forward to the date of the presentation. This summary would encapsulate the materials and methods the three authors used in coming to the conclusion they did. The presentation would then set out the various Government of China statements which refer to and rely on anti-rejection drug statistics as an indicator of transplant volume in China.The presentation third would look at the publicly available information about importation of anti-rejection drugs into China, production of anti-rejection drugs in China and transplant tourism into China. The presentation would consider fourth data compiled by Quintiles IMS, an American health care information company. The methodology the company uses in accumulating its data on anti-rejection drugs would be examined. In assessing the company data, the presentation would consider other countries besides China and compare the projection in transplant volumes the company data generates with independently verifiable actual volumes. Results and Discussion The result would be that anti-rejection drug statistics do not sustain the Chinese claims of lower transplant volumes than demonstrated by the Matas-Kilgour-Gutmann update. The Matas-Kilgour-Gutmann update conclusion of transplant volumes in China of 60,000 to 100,000 a year remains effectively unanswered. Conclusion The United Nations Committee against Torture, the European Parliament and the United States Congress House of Representatives have all called for the Government of China to cooperate with an independent investigation into the sourcing of organs in China. In light of the Matas-Kilgour-Gutmann update and the inability of the Government of China to answer effectively its conclusion, these calls for the Government of China to cooperate with an independent investigation into the sourcing of organs in China remain valid.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".