Evaluating the Potential Socio-Economic Impact of Personalized Medicine
Bibliographic record
Abstract
Since 2000, Québec and Canada have made significant R&D investments in the area of genomics, with a particular focus on technological platform development and genetics. To justify these and future major investments in genomic research, clear benefits of genomic technologies to society must be demonstrated. The goals of the study are to provide methodology to evaluate potential socio-economic impact of personalized medicines, to demonstrate it on two applications of genomic technology and to summarize obstacles to realize the potential socio-economic benefits of genomic research. The following report is the presentation of the project made at Genome Quebec. Depuis 2000, le Québec et le Canada ont investi considérablement en R.-D. dans le domaine de la génomique, en mettant l'accent sur l'élaboration d'une plateforme technologique et sur la génétique. Afin de justifier ces investissements et d'autres à venir dans la recherche génomique, les bénéfices évidents des technologies génomiques pour la société doivent être démontrés. Les objectifs de notre étude est d'offrir une méthodologie pour évaluer l'impact socioéconomique potentiel de la médecine personnalisée, d'en démontrer la méthodologie à l'aide de deux applications de la technologie génomique et d'en résumer les obstacles à la réalisation des bénéfices liés à la recherche génomique. Le rapport qui suit est la reproduction de la présentation du projet qui a été faite à Génome Québec.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".