Stem cells patents: main features and citation analysis
Bibliographic record
Abstract
This is a quantitative, patentometric, descriptive study that makes use of production and citation indicators to achieve specific purposes. Based on Price's square root law, the 181 most cited patents in the area of stem cells were the object of this study. After the collection of patents, information was extracted so that it could meet specific objectives, such as priority number, international patent classification, non-patent citation, and patent citation. It has been found that 80% of patents are from US-based companies; 6.7% are based in Japan; 3.6% in England, about 2% in France and Switzerland; 1.2% in Denmark and Ireland, and the others (3.3%) are distributed among companies in the Netherlands, Austria, Germany, China and Canada. Regarding the International Patent Classification (IPC), the patents of this research were classified into 5 sections, of which the area of human needs outstanded with 52.51%. The 181 patents found performed a total of 6,970 citations to other patents, of which 84 were the most cited. The patents that received the highest number of citations were US5486359-A and US6200806-B1, with 40 occurrences (0.26%), both filed in the US office. A hundred forty one out of the 181 patents cited non-patent documents (CR field). It was observed that, based on what could be identified, most citations of non-patent documents are citations of scientific journals (1,426), totaling 7,701 articles cited (+/- 5.5 articles/journal). Patent documents contain important information for understanding the development of science and technology. It is concluded that patents are important sources to be analyzed and that more detailed studies of what is cited in patents should be carried out.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".