Identification of quality‐of‐life clusters by the Quebec sleep questionnaire in sleep apnea patients
Bibliographic record
Abstract
Summary Patients with obstructive sleep apnea (OSA) may present different symptoms. The clinical importance of symptom clustering is supported by the difference in the incidence of cardiovascular diseases between hypersomnolent and non‐hypersomnolent sleep apnea patients. The objective of this study was to determine if quality‐of‐life clusters could be identified from the Quebec Sleep Questionnaire (QSQ) in OSA patients. Latent class analysis was used to identify clusters in a multivariate analysis of dichotomic variables (presence or absence of symptoms) for each item the QSQ obtained from 147 patients who fulfilled the questionnaire during its validation and subsequent trials (75.5% males, age: 53 ± 11 years, body mass index (BMI): 30.4 ± 4.7 kg/m2, apnea/hypopnea index (AHI): 31.3 ± 14.8/h). Three clusters were identified. Quality of life was preserved in patients of cluster 1 (20.4% of patients). Patients of cluster 2 (32.6% of patients) had a moderately impaired quality of life, mainly due to daytime somnolence and poor sleep quality. Patients with impaired quality of life (cluster 3, 46.9% of patients) had an important impact in every domain of the QSQ with the highest sleepiness and daytime symptom impairments. Gender, BMI, and AHI did not differ between the three clusters. In conclusion, different quality‐of‐life clusters can be identified from the QSQ in sleep apnea patients. These clusters are similar to those reported previously. Further studies are needed to validate these clusters in larger and independent cohorts, to evaluate how they respond to OSA treatment, and their relationship with incident outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".