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Record W4378611558 · doi:10.1093/sleep/zsad077.0499

0499 Identifications of symptoms clusters by the Quebec Sleep Questionnaire in sleep apnea patients

2023· article· en· W4378611558 on OpenAlexaffabout
Frédéric Sériès, Annie C. Lajoie, Yves Lacasse

Bibliographic record

VenueSLEEP · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
Fundersnot available
KeywordsQuality of life (healthcare)MedicineCluster (spacecraft)Sleep apneaDiseaseSleep (system call)Incidence (geometry)ApneaPediatricsInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Abstract Introduction Sleep apnea (SA) patients may present different symptom clusters. The clinical importance of such clustering is supported by the differences in the incidence of cardiovascular diseases between hypersomnolent and non hypersomnolent patients. The Quebec Sleep Questionnaire (QSQ) has been elaborated 20 years ago that documents quality of life in SA patients by measuring the impact of the disease on five different domains (nocturnal and diurnal symptoms, hypersomnolence, emotions and social interactions). Previous cluster analyses took into account the frequency of typical SA symptoms. Many, but not all symptoms selected in these studies are addressed in the QSQ. The objective of this study is to determine if quality of life clusters can be identified using responses collected from the QSQ in SA patients. Methods A cluster analysis was completed in symptom scores collected from the QSQ+ Epworth scores in147 patients who filled in the questionnaire during its validation (75.5 % males, age: 53 ± 11 y, BMI: 30.4 ± 4.7 Kg.m2, AHI 31.3 ± 14.8 /h). For each item, patients were considered as symptomatic for scores ≤ 4 (the lowest the score the higher the impact) and for ESS > 10. Results Three clusters were identified: patients in cluster 1 (20.4 % of patients) can be identified as minimally symptomatic in each domain. In the cluster 2 (32.6 %), quality of life was mainly impacted by hypersomnolence with disturbed sleep. Patients in cluster 3 (46.9 %) reported important impacts on their quality of life in each domain of the QSQ. Gender, BMI and AHI did not differ between the three clusters. However, patients in cluster 1 were significantly younger than those of cluster 2 (50.5 ± 9.9 and 57.6 ± 11.9 years, respectively, p = 0.01). Conclusion Different clusters of quality of life can be identified by the QSQ in SA patients. These clusters seem to have close similarities with previously reported symptom frequency driven clusters. The relationship between quality of life symptom clusters and long-term clinical outcomes should be explored in a large prospective cohort of sleep apnea patients. Support (if any)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.283
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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