Measurement of Health-Related Quality of Life in Survivors of Cancer in Childhood in Central America: Feasibility, Reliability, and Validity
Bibliographic record
Abstract
Cancer is the commonest cause of disease-related death in children over 5 years of age in various parts of Latin America, but the survival rates are improving. This study assessed the health status and health-related quality of life (HRQL) of more than 200 survivors of cancer in childhood in the countries of a Central American consortium devoted to pediatric hematology-oncology. Patients' self-reports and parental proxy assessments were collected using interviewer-administered Spanish-language questionnaires, and physicians provided assessments using self-complete questionnaires, based on the complementary Health Utilities Index (HUI) Mark 2 (HUI2) and Mark 3 (HUI3) health status classification systems. Inter-rater agreement, measured by intra-class correlation (ICC), was fair to moderate (0.34 0.60) for all 3 pairs of assessors for readily assessable attributes: HUI2 sensation, HUI3 vision, HUI3 hearing, and HUI3 ambulation. Less than 40% of the patients reported being in perfect health. More than 20% reported being in health states with HRQL scores corresponding to moderate or severe disability, notably in the attributes of emotion and cognition. The results reflect a common profile in survivors of cancer in childhood, including those from industrialized societies. This study illustrates the feasibility of collecting reliable and valid information on HRQL in the developing country context, raising the prospect that such information could be used to influence clinical practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".