Information mining in patent filings on injectable antineoplastics as a contribution to Health Policy
Bibliographic record
Abstract
Introduction: According to data from the United Nations, cancer is the second leading cause of death in the world. Currently, information management has been increasingly difficult due to the large amount of data to be managed. In general, the databases that store patent documents make it possible to read them in full, but do not allow the extraction and treatment of large amounts of data. In this sense, it is necessary to use management software. Objective: To identify, extract, process the data, organize, and make available, in the form of graphical interfaces, the technological information on injectable oncology described in the current patents. Methodology: Patents deposited between January 2002 and July 2022 were analyzed using the ORBIT Intelligence® platform. In the “Advanced Search” field, the “Title, Abstract” filters were applied and the search terms: “injectable AND cancer” were used. Results and Discussion: 115 patent families were identified. The USA stands out in the number of patent documents filed, presenting a total of 56 documents. Inventors Ivan Edward Hofman, Farber Michael, Franco Rodriguez Guillermo and Gutierro Aduriz Ibon were the most productive, each with 3 documents deposited. The institutions Bespoke Bioscience (USA), Immunocore Holdings (United Kingdom) and Mountain Valley MD Holding (Canada) stood out, each holding 3 documents. In the documents analyzed, the most recurrent technological domain went beyond the "pharmaceutical" technological domain, which obtained 109 documents and others such as chemical, biological, electrical, micro and nanotechnology. Final Considerations: The results obtained by mining the data extracted from patent documents proved to be efficient and, can be useful as an effective tool to analyze, compare and monitor research and innovation activities in injectable oncology.
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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".