Bibliographic record
Abstract
Pharmacogenomics has the potential to improve patient-centered care and lead to an overall decrease in healthcare costs. This would be achieved through fewer hospitalizations due to adverse drug reactions, individualized and effective therapies, and decreased drug development costs with single nucleotide polymorphism pre-screening. Although challenges do exist in encouraging the use of pharmacogenomics―specifically in regards to resources, regulation, and impacts on the pharmaceutical industry―the benefits may outweigh the costs in terms of patient health and safety. In implementing pharmacogenomics, various clinical, ethical, legal, social and economical factors must be considered. La pharmacogénomique aurait le potentiel de diminuer le coût des soins de santé et d’optimiser les soins primaires aux patients. Il serait possible d’y parvenir en réduisant les hospitalisations suite aux effets secondaires associés aux médicaments, en utilisant des thérapies individualisées plus efficaces et en diminuant le coût associé au développement de médicaments grâce au test de dépistage de polymorphisme d’un seul nucléotide. Malgré les défis associés à l’utilisation de la pharmacogénomique surtout sur le plan des ressources, de la régulation et de l’impact dans l’industrie pharmaceutique, les avantages en terme de santé et de sécurité sont à considérer. Plusieurs facteurs cliniques, éthiques, légaux, sociaux, et économiques doivent être pris en considération pour l’utilisation de la pharmacogénomique.
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 | 0.050 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".