The Quebec Sleep Questionnaire on quality of life in patients with obstructive sleep apnea: translation into Portuguese and cross-cultural adaptation for use in Brazil
Bibliographic record
Abstract
OBJECTIVE: To translate the Quebec Sleep Questionnaire (QSQ), a specific instrument for the assessment of quality of life in patients with sleep obstructive apnea, into Portuguese and to create a version that is cross-culturally adapted for use in Brazil. METHODS: The Portuguese-language version was developed according to a rigorous methodology, which included the following steps: preparation; translation from English into Portuguese in three versions; reconciliation to a single version; back-translation of that version into English; comparison and harmonization of the back-translation with the original version; review of the Portuguese-language version; cognitive debriefing; text review; and arrival at the final version. RESULTS: The Portuguese-language version of the QSQ for use in Brazil had a clarity score, as measured by cognitive debriefing, ranging from 0.81 to 0.99, demonstrating the consistency of translation and cross-cultural adaptation processes. CONCLUSIONS: The process of translating the QSQ into Portuguese and creating a version that is cross-culturally adapted for use in Brazil produced a valid instrument to assess the quality of life in patients with obstructive sleep apnea.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.000 |
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".