BREAST-Q Translation and Linguistic Validation to European Portuguese
Bibliographic record
Abstract
INTRODUCTION: Evaluating the impact of surgical treatment on health-related quality of life of breast cancer patients has become increasingly relevant, particularly for reconstructive procedures. The BREAST-Q consists of a broadly used patient-reported outcome measure to assess the impact of breast surgery on the health-related quality of life of these patients. The aim of this study was to translate and linguistically validate the BREAST-Q reconstructive module to European Portuguese. MATERIAL AND METHODS: The translation and linguistic validation process was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and started after obtaining permission from the original authors (developers). It involved two direct English to European Portuguese translations and a back translation, maintaining conceptual and cultural equivalence, an expert panel discussion, cognitive interviews with five patients and a final consensus. RESULTS: The forward translations led to the revision of three conceptually distinct items. The backward translation resulted in predominantly wording discrepancies and the three conceptual disparities noted in the back translation were revised on a consensual version. All material was openly discussed with the original authors and in an expert panel meeting. One item was changed after the cognitive interviews. The final consensual version was obtained. CONCLUSION: This stepwise approach allowed to linguistically validate the BREAST-Q reconstructive module to European Portuguese so that it can be used in the Portuguese population. Additionally, the applied methodology may serve to support and guide other instruments for linguistic validation.
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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 | 0.035 | 0.066 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".