Modeling complex relationships in laryngeal pathologies: A structural equation analysis of dysphonia in Saudi Arabian patients
Bibliographic record
Abstract
INTRODUCTION: Dysphonia is a prevalent laryngological condition that impairs communication and quality of life in several cases, however the interplay between risk factors and specific laryngeal pathologies remains poorly understood. Previous studies often lack structured modeling of these multifactorial interactions. We utilized structural equation modeling (SEM) in a large Saudi-based cohort to investigate the direct and indirect pathways linking demographic factors, behavioral variables, as well as the types of laryngeal pathologies. METHODS: Our retrospective cohort study retrieved and analyzed the relevant data from dysphonia patients at a tertiary-care hospital in Riyadh, Saudi Arabia. Laryngeal pathologies were categorized into structural, inflammatory, neurological, and functional types. Our proposed SEM framework has assessed both of the direct and indirect pathways from risk factors to these categories, including cross-pathology links and moderation effects. RESULTS: Our study cohort has included a total of 998 eligible dysphonia patients. Structural pathologies were most prevalent (33.9 %). The SEM demonstrated excellent fit (CFI = 0.961, RMSEA = 0.049) and identified significant pathways: female gender strongly predicted structural (β = 0.412) and functional pathologies; increasing age associated positively with inflammatory, neurological, and functional types; smoking strongly predicted inflammatory pathologies (β = 0.338); occupational voice use predicted structural (β = 0.356) and functional (β = 0.297). CONCLUSIONS: This SEM-based etiological model reveals peculiar and significant important dysphonia pathways with clear demographic signatures in a Saudi-based population. Findings highlight specific high-risk groups (as female voice professionals, older smokers) and important progression patterns (inflammatory-to-neurological, structural-to-functional). This framework helps to offer a more precise risk profiling, supporting targeted screening protocols and tailored preventive interventions based on specific risk factors and interconnected pathology development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".