Precision Oncology: A Global Perspective on Implementation and Policy Development
Bibliographic record
Abstract
Despite the acknowledged merits of precision oncology (PO) and its increasing global implementation, its full potential for advancing care and prevention remains unrealized. The benefits are currently accessible to only limited patient segments because of multifaceted barriers. Successful implementation hinges on various factors-scientific complexities not limited to technical, clinical, regulatory, economic, administrative, and health care policy-related challenges. From building infrastructure to the associated costs, including research and development, testing, processing, and trained personnel, a lack of alignment persists. Administrative alignment with regulatory and payor acceptance is crucial. Health care policy must adapt to the ongoing shift from a one-size-fits-all treatment to a personalized approach. Without official endorsement of long-term gains over short-term costs and the health establishment's readiness for innovation, PO prospects, even in prosperous economies, may stagnate. Lower-income countries face exacerbated challenges, intensifying barriers to adoption. Nevertheless, growing awareness and utilization, driven by recognized potential for patients and public health, along with successful examples and advocacy, are progressively influencing policy for a more inclusive and beneficial approach to PO adoption.
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 | 0.040 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".