Bibliographic record
Abstract
This study aimed to evaluate the potential of pharmacogenomic science and technology (S&T) in Canada. In order to do so, the multiple scientific and ethical issues related to pharmacogenomic research were reviewed. Scientometric and technometric analyses were performed to provide a quantitative evaluation of Canada's performance in this field relative to that of the world and also within Canada. The position of pharmacogenomic S&T within the context of the healthcare industry was also discussed. Finally, Canada’s main strengths and weaknesses together with future opportunities and threats were summarized in order to establish the potential of pharmacogenomic S&T in Canada and to provide recommendations so that Canada can benefit from this potential. The scientometric analysis is based on papers retrieved from the Medline database using a set of keywords defining the field. It was conducted using time series (1991-2002) to outline the evolution of Canada's scientific output in pharmacogenomics both at the international level (G7 countries) and national levels. The performances of the different entities at the international and national levels are presented according to three scientometric indicators. The technometric analysis is based on patents retrieved from the United States Patents and Trademark Office (USPTO) database and was conducted using time series (1999-2003) to outline the evolution of Canada's technological output in pharmacogenomics both at the international level (G7 countries) and national levels. The
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.382 | 0.187 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".