Parents’, Health Care Professionals’, and Scientists’ Experiences of a Precision Medicine Pilot Trial for Patients With High-Risk Childhood Cancer: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: Children with high-risk cancers have low survival rates because current treatment options are limited. Precision medicine trials are designed to offer patients individualized treatment recommendations, potentially improving their clinical outcomes. However, parents' understanding is often limited, and expectations of benefit to their own child can be high. Health care professionals (HCPs) are often not familiar with precision medicine and might find managing families' expectations challenging. Scientists find themselves working with high expectations among different stakeholders to rapidly translate their identification of actionable targets in real time. Therefore, we wanted to gain an in-depth understanding of the experiences of all stakeholders involved in a new precision medicine pilot trial called TARGET, including parents, their child's HCPs, and the scientists who conducted the laboratory research and generated the data used to make treatment recommendations. METHODS: We conducted semistructured interviews with all participants and analyzed the interviews thematically. RESULTS: We interviewed 15 parents (9 mothers; 66.7% bereaved), 17 HCPs, and 16 scientists. We identified the following themes in parents' interviews: minimal understanding and need for more information, hope as a driver of participation, challenges around biopsies, timing, and drug access, and few regrets. HCP and scientist interviews revealed themes such as embracing new technologies and collaborations and challenges managing families' expectations, timing of testing and test results, and drug access. CONCLUSION: Educating families, HCPs, and scientists to better understand the benefits and limitations of precision medicine trials may improve the transparency of the translation of discovery genomics to novel therapies, increase satisfaction with the child's care, and ameliorate the additional long-term psychosocial burden for families already affected by high-risk childhood cancer.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".