Measuring disease-specific quality of life in rare populations: a practical approach to cross-cultural translation
Bibliographic record
Abstract
BACKGROUND: Disease-specific quality of life (QoL) measures have enhanced the capacity of outcome measures to evaluate subtle changes and differences between groups. However, when the specific disease is rare, the cohort of patients is small and international collaboration is often necessary to accomplish meaningful research. As many of the QoL measures have been developed in North American English, they require translation to ensure their usefulness in a multi-cultural and/or international society. Published guidelines provide formal methods to achieve cross-culturally comparable versions of a QoL tool. However, these guidelines describe a rigorous process that is not always feasible, particularly in rare disease groups. The objective of this manuscript is to describe the process that was developed to achieve accurate cross-cultural translations of a disease-specific QoL measure, to overcome the challenges of a small sample size, i.e. children with a rare disorder. PROCEDURE: A measurement study was conducted in the United Kingdom (UK), France, Germany and Uruguay, during which the validated measure was translated into the languages of the respective countries. RESULTS: This is a report of a modified, child-centric, cross-cultural translation and adaptation process in which culturally appropriate and methodologically valid translations of a disease-specific QoL measure, the Kids' ITP Tools (KIT), were performed in children with immune thrombocytopenic purpura (ITP). The KIT was translated from North American English into UK English, French, German, and Spanish. CONCLUSION: This study was a successful international collaboration. The modified process through which culturally appropriate and methodologically valid translations of QoL measures may be achieved in a pediatric population with a relatively rare disorder is reported.
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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 | 0.181 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".