Quality of life in childhood advanced cancer: from conceptualization to assessment with the Advance QoL tool
Bibliographic record
Abstract
BACKGROUND: Advanced childhood cancer, a condition with no available cancer-focused treatment options, greatly impacts Quality of Life (QoL). We need appropriate assessment strategies to select adapted treatment targets, improve care and optimize communication. Our first goal was to identify the domains of patients' QoL by combining for the first time the perspectives of patients and parents with previously collected reports in professionals. Our second goal was to develop a simple QoL assessment tool and optimize its format and content for use in the childhood advanced cancer population. METHODS: To identify QoL domains, we conducted qualitative interviews with 7 young patients (4 girls, 3 boys, aged 13 ± 4 yrs) and 9 parents (7 mothers, 2 fathers) from our treatment centre. We used inductive thematic content analysis to code and categorize respondents' viewpoints. The first version of the tool (Advance QoL) was then drafted, and structured feedback was collected through interviews and a survey with 15 experts. We computed content validity indices. RESULTS: Apart from the physical, psychological, and social domains, participants insisted on four original themes: autonomy, pleasure, the pursuit of achievement, and the sense of feeling heard. This was in line with the categories found in a preliminary study involving professionals (PMID: 28137343). Experts evaluated the tool as clear, relevant, acceptable, and usable. They formulated recommendations on instructions, timeframe, and item formulations, which we implemented in the refined version. CONCLUSIONS: Advance QoL is an innovative tool targeting key life domains in childhood advanced cancer. It is focused on preserved abilities and targets of care. The refined version is appropriate for adult respondents within families and professionals. Future studies will develop versions for young ages to collect the experience of patients themselves. This will open on future reliability, validity, sensitivity, and implementation studies.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".