Health-related Quality of Life: Changes in Children Undergoing Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Information regarding changes in the health-related quality of life (HRQL) of children during chemotherapy is scarce. Furthermore, there exists a general lack of consensus as to which measures are best suited to assess changes in HRQL in this population. The purpose of this study is to compare the responsiveness of 3 pediatric HRQL measures: the Pediatric Quality of Life Inventory (PedsQL), the Child's Health Questionnaire (CHQ), and the Health Utilities Index (HUI). METHODS: Consecutive pediatric oncology patients and their parents completed the questionnaires at 1-week intervals for a total of 4 weeks, starting on the third day of the patient's chemotherapy treatment cycle. RESULTS: Twenty-nine patients were enrolled with the majority (62%) having a diagnosis of leukemia with an average age of 9 years. The parent proxy reports from time 1 to 4 showed a mean change in the PedsQL of 17 for the generic core scale and 12 for the cancer specific module. The mean change in CHQ physical functioning scale was 6, while the psychosocial scale was only 2, while the HUI 2 was 3 (x100), and HUI 3 was 4 (x100). There was significantly more change in the PedsQL generic scores when compared with the HUI 2 and 3 and the CHQ psychosocial scale (P<0.01). CONCLUSIONS: When measuring HRQL repeatedly in a heterogeneous population, the PedsQL is the measure most responsive to change.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".