Bibliographic record
Abstract
There is widespread concern internationally and within Canada about the rapid escalation in pharmaceutical costs. Although there is reason to believe that the quality of prescribing has improved in recent years, with heightened emphasis on evidence-based therapeutic decision-making, there is enormous pressure to prescribe in almost every clinical situation. Busy clinicians need improved tools to aid therapeutic decision-making. Access to timely information about drug efficacy and safety is essential. Most importantly, there is a need for a new partnership model that may blend the interests of patients, professionals, payors and manufacturers to better define disease state management approaches that will lead to an optimal return on the investment in pharmaceutical care. The new model will depend on high standards of research to show what does and does not work to secure the most effective pharmacotherapy. It will also require renewed efforts in education for patients and caregivers, and progress on that front will, in turn, rely on the most effective use of expanded capacity in information technology. One early impact will be seen in the reduction of medication errors. The framework for therapeutic decision-making must evolve in keeping with the revolution in human biology. With improved understanding of human genomics and proteomics, prescribers are better able to consider highly individualized and targeted drug therapies while actively concentrating on risk minimization. The Optimal Drug Therapy National Symposium 2001 has created a consensus among stakeholders and pointed the way to improvements in drug therapy that may be achieved through enhanced research, educa- tion, public involvement and professional support. Unequivocal commitment to the laudable goal of optimal drug therapy is now required from all sides.
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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.070 | 0.053 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".