THE NEW CHIMAERA: THE INDUSTRIALIZATION OF ORGAN TRANSPLANTATION
Bibliographic record
Abstract
Clinical organ transplantation has evolved through advances in patient care in parallel with investigations in associated biologies. It has developed from a cottage industry to an important medical specialty driven increasingly by the availability of newer and more effective immunosuppressive drugs, and dependent on consistently close collaborations between university-based clinical scientists and the pharmaceutical industry. Particularly during the past decade, however, this industry has undergone striking changes, consolidating into huge multi-national corporations, each competing for patients, their doctors, and for support of the allied hospitals. Because of the growth of "Big Pharma," the relationship between academia and industry has changed. There have been many advantages to such mutually dependent interactions. A combination of university-based expertise and the specialized knowledge and resources of industry have produced important scientific gains in drug development. Commercial sponsorship of applied research has been crucial. The orchestration of multicenter controlled clinical drug trials has provided invaluable information about the effectiveness of newer agents. But there are also disadvantages of increasing concern. Indeed, the power of "Big Pharma" in many medical fields including transplantation is such that presentation of data can be delayed, adverse results withheld, and individual investigations hampered. Clinical trials may be protracted to stifle competition. Monetary considerations may transcend common sense. Several measures to enhance the clinical relationship between the pharmaceutical industry and those involved with organ transplantation are suggested, particularly the use of third party advisors in the production of clinical trials, support for more basic research and in the dissemination of results. In this way, the increasingly problematic phenomenon of commercialization of the field of transplantation can be tempered and controlled.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".