German translation of the Characterizing Freezing of gait questionnaire: implementation of the TRAPD process
Bibliographic record
Abstract
BACKGROUND: Freezing of Gait (FOG) is a severe symptom of Parkinson’s Disease (PD) that affects mobility and quality of life. The ‘Characterizing Freezing of Gait Questionnaire’ (C-FOG) is an assessment tool for screening and determining subtypes of FOG. However, it currently only exists in the English language. This study aimed to translate the C-FOG from English to German METHODOLOGY: This project was conducted from September 2024 to February 2025 at a University of Applied Sciences in Krems, Austria and applied the Translation, Review, Adjudication, Pretest, and Documentation (TRAPD) process. This included professional translations, 1 review-workshop with 4 participants, adjudication, pre-testing in 2 workshops with 11 participants, including 8 healthcare workers and 3 people with Parkinson’s disease, and parallel detailed documentation of all changes and decisions. RESULTS: In the first translation stage of the C-FOG two professional translators provided two different preliminary German translations. The original questionnaire was divided into 62 translation units to provide a detailed descriptive analysis of the adaptations made in each step of the translation process. During the review stage, 31 units (50%) of the preliminary translations required adaptation. During the pre-test stage, the participants discussed the German translation of the C-FOG that had been adjudicated in the previous stage. This resulted in the need for further adaptations of 25 translation units (40%), while 37 units (60%) required no additional changes. Following back-translation by a professional translator and feedback from the first author of the original questionnaire, the German version of the C-FOG, the C-FOG-D, was finalized. CONCLUSION: This study provides a systematically conducted German translation of the Characterizing Freezing of Gait Questionnaire using the TRAPD process to ensure understandability and equivalence to the original questionnaire. A full consensus was reached among the participants involved in the final translation stage. For future validation studies and potential modifications to the questionnaire, it is necessary to include more people with PD experiencing FOG. A larger sample size could offer a more comprehensive perspective on how individuals differ in their perceptions of FOG, and to what extent the language used in the questionnaire effectively captures these experiences.
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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 | 0.042 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".