Quality of Life (QoL) of Children and Adolescents Participating in a Precision Medicine Trial for High-Risk Childhood Cancer
Bibliographic record
Abstract
Precision medicine is changing the treatment of childhood cancer globally, however little is known about quality of life (QoL) in children and adolescents participating in precision medicine trials. We examined QoL among patients enrolled in PRISM, the Zero Childhood Cancer Program’s precision medicine trial for high-risk childhood cancer. We assessed patient QoL via self-report (aged 12–17 years) and parent-proxy (aged 4–17 years) completion of the EQ-5D-Y. We analysed data using descriptive statistics and regression models. Patients (n = 23) and parents (n = 136) provided data after trial enrolment and following receipt of trial results and treatment recommendations (n = 8 patients, n = 84 parents). At enrolment, most patients were experiencing at least some difficulty across more than one QoL domain (81% patient self-report, 83% parent report). We did not find strong evidence of a change in QoL between timepoints, or of demographic or disease factors that predicted parent-reported patient QoL (EQ-VAS) at enrolment. There was strong evidence that receiving a treatment recommendation but not a change in cancer therapy was associated with poorer parent-reported patient QoL (EQ-VAS; Mdiff = −22.5, 95% CI: −36.5 to −8.5, p = 0.006). Future research needs to better understand the relationship between treatment decisions and QoL and would benefit from integrating assessment of QoL into routine clinical care.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".