Challenges adopting next-generation sequencing in community oncology practice
Bibliographic record
Abstract
PURPOSE OF REVIEW: We are in an exhilarating time in which innovations exist to help reduce the impact of cancer for individuals, practitioners and society. Innovative tools in cancer genomics can optimize decision-making concerning appropriate drugs (alone or in combination) to cure or prolong life. The genomic characterization of tumours can also give direction to the development of novel drugs. Next-generation tumour sequencing is increasingly becoming an essential part of clinical decision-making, and, as such, will require appropriate coordination for effective adoption and delivery. RECENT FINDINGS: There are several challenges that will need to be addressed if we are to facilitate cancer genomics as part of routine community oncology practice. Recent research into this novel testing paradigm has demonstrated the barriers are at the individual level, while others are at the institution and societal levels. SUMMARY: This article, based on the authors' experience in community oncology practice and summary of literature, describes these challenges so strategies can be developed to address these challenges to improve patient outcomes.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".