Bibliographic record
Abstract
This case tells the saga of the famous anticancer drugs Opdivo and Yervoy and their contribution to creating a new treatment category of immuno-oncology. In 1987, the French immunologist Pierre Golstain discovered and cloned the CTLA-4 gene. In 1992, Tasuku Honjo, a professor at Kyoto University in Japan, found a protein called PD-1 on the surface of T cells in mice. These two findings laid the initial foundation for immune checkpoint theory and checkpoint blockade therapy. Many pharmaceutical companies and scientists have invested much time, money, and energy to develop a new generation of tumor immunotherapy on these two cornerstones. However, developing a new drug is fraught with uncertainty, and any progress is a matter of trial and error. Opdivo and Yervoy's "predecessors" have endured many such fates; luckily, they survived through scientists' best efforts each time. Since 2007, the multinational pharmaceutical company BMS has launched a "String of Pearls" M&A strategy. This strategy seeks to gradually integrate external innovation and expand its own capabilities through small and medium acquisitions, avoiding large acquisitions. BMS has cast a wide net over small and medium-sized research and development (R&D) biopharmaceutical firms. They don't care that most of the beads they catch are small or even worthless as long as there are one or two large beads with commercial value. In 2009 BMS launched a takeover bid for Medarex, a mid-sized pharmaceutical company. BMS was interested in the CTLA-4 antibody project ipilimumab (the "predecessor" of Yervoy) in the Medarex R&D pipeline. A Medarex scientist (Nils Lonberg) was more bullish on the PD-1 antibody project Nivolumab (the "predecessor" of Opdivo), which was in phase I clinical trials. He reminded Medarex's management to allocate more value to this project in negotiations. However, Nivolumab, which needed to be taken more seriously, was almost half bought and given to BMS. Because of the considerable uncertainty in the R&D of new drugs, the acquisition is often like a game of guessing beads. Everything is in a blind box waiting to be opened. What kind of pearls will BMS get after opening the blind box?
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".