The Translation and Cultural Adaptation of LYMPH-Q Upper Extremity Module to the Indonesian Language
Bibliographic record
Abstract
Background: The LYMPH-Q Upper Extremity module is a patient-reported outcome measurement tool developed by the Q-Portfolio team at McMaster University, Canada, and is widely used to determine the health-related quality of life of patients with upper extremity lymphedema. However, the translation of these patient-reported outcome measurement tools to the Indonesian language has not been attempted by any institution. Methods: The Indonesian translation of the LYMPH-Q Upper Extremity module was performed according to the International Society of Pharmacoeconomics and Outcomes Research guidelines. The steps included forward translation and reconciliation, back translation and review, and cognitive debriefing with cultural adaptation. The respondents in this study were recruited from Dr. Cipto Mangunkusumo Hospital and the Indonesian LYMPH-Q project community. Results: A total of 2.94% of the forward-translated items were discordant at the reconciliation meeting. During the back translation review, 4 of 102 items were discordant between the original items and the back translation result. This study also emphasized Indonesian respondents' understanding of the translated items, which were influenced by sociodemographics and religious beliefs tailored specifically to Indonesian characteristics. Conclusions: The Indonesian translation of the LYMPH-Q Upper Extremity module has already been conducted according to the International Society of Pharmacoeconomics and Outcomes Research guidelines, and future validation studies are necessary.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".