Unique Challenges of Hebrew Translation and Cross-Cultural Adaptation of LIMB-Q Kids for Children with Lower Limb Differences
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Patient-Reported Outcome Measures (PROMs) capture patients' perspectives about their health status, quality of life, and medical care outcomes. LIMB-Q Kids is a validated PROM designed to assess health-related quality of life (HRQL) in children with lower limb differences. It evaluates physical, social, and psychological function; symptoms related to the leg, hip, knee, foot and ankle; leg-related distress, appearance, and school-related concerns. It has now been translated and culturally adapted from English to other languages. The aim of this study was to translate and culturally adapt LIMB-Q Kids to Hebrew. METHODS: Following international guidelines, two independent forward translations from English to Hebrew were produced and reconciled into a single version. A backward translation was then compared with the original to identify discrepancies. This draft underwent cognitive debriefing interviews (CDIs) with 11 children (ages 8-15) having lower limb differences to assess comprehension and cultural relevance. RESULTS: CDIs revealed general issues including lengthy or unclear text, high-level language, cultural unfamiliarity and duplication of descriptors. Specific to Hebrew, issues included gender inflections and the need for vowel diacritics to support younger, less proficient readers. Revisions to 14 items were made. CONCLUSIONS: A translation and cultural adaption (TCA) process led to a linguistically validated and culturally adapted Hebrew version of LIMB-Q Kids. It can now be used for the clinical follow-up of children with lower limb differences including pre- and post-operatively, and as an aid to decision-making for surgery.
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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 | 0.096 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".