Precision Medicine Is Changing the Roles of Healthcare Professionals, Scientists, and Research Staff: Learnings from a Childhood Cancer Precision Medicine Trial
Bibliographic record
Abstract
Precision medicine programs aim to utilize novel technologies to identify personalized treatments for children with cancer. Delivering these programs requires interdisciplinary efforts, yet the many groups involved are understudied. This study explored the experiences of a broad range of professionals delivering Australia's first precision medicine trial for children with poor-prognosis cancer: the PRecISion Medicine for Children with Cancer (PRISM) national clinical trial of the Zero Childhood Cancer Program. We conducted semi-structured interviews with 85 PRISM professionals from eight professional groups, including oncologists, surgeons, clinical research associates, scientists, genetic professionals, pathologists, animal care technicians, and nurses. We analyzed interviews thematically. Professionals shared that precision medicine can add complexity to their role and result in less certain outcomes for families. Although many participants described experiencing a greater emotional impact from their work, most expressed very positive views about the impact of precision medicine on their profession and its future potential. Most reported navigating precision medicine without formal training. Each group described unique challenges involved in adapting to precision medicine in their profession. Addressing training gaps and meeting the specific needs of many professional groups involved in precision medicine will be essential to ensure the successful implementation of standard care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".