LIMB-Q Kids—German Translation and Cultural Adaptation
Bibliographic record
Abstract
(1) Purpose: Lower limb deformities can have a severe impact on health-related quality of life (HRQL). LIMB-Q Kids is a new patient-reported outcome measure (PROM) aiming to elucidate the experience of 8-18-year-old patients before, during and after treatment, and to measure the different aspects of HRQL. The aim of this study was to translate and culturally adapt LIMB-Q Kids to German. (2) Methods: The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines were followed. Three forward translations, a backward translation, an expert panel meeting with eight participants, and twenty cognitive debriefing interviews led to the final German version of LIMB-Q Kids. (3) Results: In the forward translations, 4/159 items were difficult to translate, and 2/159 items in the backward translation differed from the original English version. Cognitive debriefing interviews with 20 patients identified 7/159 items that were difficult to comprehend/answer, and 2 of these items were changed. (4) Conclusions: Lower limb deformities can have a great impact on children, and it is important to measure and consider the impact on HRQL. In order to be able to use PROMs in different countries, conceptually equivalent translations and cultural adaptations should be performed in order to ensure comprehensibility. The final German version of LIMB-Q Kids is ready for use in an international field test.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".