Life sciences licensing deals in the first quarter of 2018: updatesand trends
Bibliographic record
Abstract
During the first quarter of 2018, Cortellis Competitive Intelligence registered 879 new deals (excluding mergers and acquisitions) with a total disclosed deal value of approximately USD 35.2 billion as part of its ongoing coverage of licensing activity in the life sciences sector. This compares to 1,203 and USD 26.2 bil-lion in the fourth quarter of 2017, and 1,158 and USD 31.8 billion in the first quarter of 2017. This meant a significant increase in the total disclosed deal value compared to these two previous periods (+34% and +10.7%, respectively), and included the USD 5.8 billion pact between Merck and Co. and Eisai which became the highest-value deal in the last 4-year opening quarters. However, during the first quarter of 2018 there was not a high number of signed agreements versus the fourth quarter of 2017 and the first quarter of 2017 (-27% and -24%, respectively), reaching a number similar to that in the first quarter of 2014 with a total of 931 agreements covered.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".