Why the Shift? Taking a Closer Look at the Growing Interest in Niche Markets and Personalized Medicine
Bibliographic record
Abstract
Pharmaceutical research and development is increasingly focused on niche markets, most notably treatments for rare diseases and "personalized" medicine. Drawing on the results of a qualitative study of 34 key Canadian stakeholders (including drug regulators, funders, scientists, policy experts, pharmaceutical industry representatives, and patient advocates), we explore the major trends that are reportedly contributing to the growing interest of the pharmaceutical industry in niche markets. Informed by both these key informant interviews and a review of the relevant literature, our paper provides a critical analysis of the many different-and sometimes conflicting-views on the reasons for and extent of the shift toward niche markets. We consider some of the potential advantages to industry, as well the important implications and risks that arise from the increasing pursuit of niche markets and pharmacogenomics. While there are many potential benefits associated with targeted therapies and drug development for historically neglected rare diseases, niche market therapies also present evidentiary challenges (e.g., smaller clinical trials and enrichment strategies) that can make approval decisions difficult, and uncertainties remain around the true benefits of many therapies.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".