German Translation and Linguistic Validation of the LIMB‑Q: A Patient-reported Outcome Measure for Lower Extremity Trauma
Bibliographic record
Abstract
Background: Lower extremity trauma can have a significant impact on a patient's quality of life. The LIMB-Q is a recently developed and validated patient-reported outcome measure that assesses patient-specific outcomes and experience of health care. The aim of this study was to translate and linguistically validate the LIMB-Q from English to German. Methods: The translation was performed by combining World Health Organization and Professional Society for Health Economics and Outcomes Research guidelines. The process consisted of forward translations, a backward translation, expert panel meetings, cognitive debriefing interviews with patients, and several rounds of discussion and reconciliation with the creators of LIMB-Q. The goal was to obtain a culturally and conceptually accurate translation of LIMB-Q into German for use in Switzerland. Results: From the two forward translations, there was one primary discrepancy between the two translators that was discussed to determine the most conceptually accurate translation. From the backward translations, there were 63 items that required discussion and re-translation. Nine patients participated in the cognitive debriefing interviews, which led to three items being modified. The translation process led to a linguistically validated and conceptually equivalent German version of the LIMB-Q. Conclusions: The German (Switzerland) version of LIMB-Q is now available. This will offer a valuable tool for lower extremity trauma research and clinical care in German-speaking populations.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".