Danish translation and linguistic validation of the LIMB-Q, a PROM for traumatic lower limb injuries and amputations
Bibliographic record
Abstract
Abstract Background The LIMB-Q is a newly developed patient-reported outcome measure (PROM), applicable for lower extremity trauma patients requiring fracture treatment, soft tissue debridement, reconstruction, and/or amputation. The aim of this study was to translate and linguistically validate the LIMB-Q from English to Danish. Method The translation and linguistic validation were performed by combining guidelines from the World Health Organization (WHO) and the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). This approach involved 2 forward translations, a backward translation, an expert panel meeting, and 2 rounds of cognitive patient interviews. The main goal of these steps was to achieve a conceptual translation with simple and clear items. Feedback from the Danish translation was used in combination with psychometric analyses for item reduction of the final international version of LIMB-Q. Results In the forward translation, 6 items were found difficult to translate into Danish. The two translations were harmonized to form the backward translation. From the backward translation, 1 item was identified with a conceptually different meaning and was re-translated. The revised version was presented at the expert panel meeting leading to revision of 10 items. The cognitive patient interviews led to revision of 11 items. The translation process led to a linguistically validated and conceptually equivalent Danish version of the LIMB-Q. Conclusion The final Danish LIMB-Q version consisting of 16 scales is conceptually equivalent to the original and ready for field-testing in Denmark. Level of evidence: Not gradable.
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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 | 0.043 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".