Bibliographic record
Abstract
Objective To study the successful experience of a leading global pharmaceutical company AstraZeneca's intellectual property right protection system,and to provide reference for the development of domestic pharmaceutical industry especially new drugs.Methods Using Chinese and foreign patent search engine Soopat database and Chinese Medicine Economic Information Nets,we retrieved and analyzed the correlation between the patent output and economic benefit for AsterZeneca globally from 2000 to 2010 and in North America(America and Canada),Japan,China from 2006 to 2010.Results It showed that the cumulative number of patent applications was significantly positively related with the annual sales income in the world,and also positively correlated in not only North America but also Japan,whereas in China it had no significant linear correlation.AstraZeneca global and regional patent applications in North America and Japan decreased with the annual sales growth rate.The patent applications in China reduced while the annual sales growth rate increased(negatives correction) from 2006 to 2010.Conclusion AstraZeneca's emphasis on the intellectual property directly affects on its economic benefit.Its steady growth in the regions of well-developed market economy is based on the creation,operation,and management of the intellectual property;its rapid progress in the regions of less-developed market economy is attributed to the patent distribution and technology transfer.It can be said that the innovation and development of drug and intellectual property protection are the main impetuses to the development of pharmaceutical companies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".