A Survey on the Present Condition of Medical Patents in Islamic Countries
Bibliographic record
Abstract
Introduction: Medicine and its technology are the most important issues in the history of Islamic countries. However, their present condition in Islamic countries is not favorable. The aim of the present research was to assess the development of medical technology in Islamic countries through studying their medical patents in United States Patent and Trademark Office (USPTO). Methods: The current research was a descriptive and applied study. The study population included all medical patents of Islamic countries registered until 2014 in the USPTO. The data were collected through combining the fields of countries’ names and the search classification, and by using the USPTO software. The required information from each patent was extracted using the USPTO 2. PATREF 5 was used for citation information and GPS Visualizer software was applied for the visualization of the geographic information map. Results: The analysis of the data showed that among the 57 Islamic countries; only 26 countries, including Malaysia, Turkey, Saudi Arabia, Iran, UAE, and Kuwait, had been active in medical inventions. The findings showed that subjects such as pharmaceuticals, organic compounds, molecular biology and microbiology, and medical and laboratory, dental, and optical, thermal, and electrical surgery equipment had the highest rank. The results also revealed that regarding medical patents, Islamic countries had the most communications with countries such as America, France, Canada, Germany, Great Britain, Japan, Malaysia, Turkey, Saudi Arabia, and Iran. Conclusion: The results of this study, in addition to providing Islamic countries’ authorities with knowledge on medical technology, and can be useful in macro and micro policies of Islamic countries in this field.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".