Socioeconomic status: a disease modifier of chronic rhinosinusitis?
Bibliographic record
Abstract
INTRODUCTION: The Lund-MacKay score (LMS) correlates poorly with chronic rhinosinusitis (CRS) symptom severity. Patients with CRS also tend to report relatively lower levels of mental wellbeing. Our purpose was to determine if there is a correlation between socio-economic status (SES) and CRS severity as measured by the LMS, and if there is an association between depression symptoms and the severity of CRS using the LMS. METHODS: A total of 127 patients diagnosed with CRS were prospectively recruited and assessed with a sinonasal assessment questionnaire (SNAQ-11), and the Patient Health Questionnaire (PHQ-9) for depression. Each patient`s education level, family income, and smoking behavior were determined. The sinus CT scan was scored using the LMS. The data were analyzed using ordinary least squares (OLS) regression techniques. RESULTS: Having a highschool education or less was associated with higher SNAQ-11 scores while being a daily smoker was associated with higher SNAQ-11 scores. There was no significant relationship between educational attainment, financial income or daily smoking and sinus CT score. Including depression scores in the SNAQ-11, regression equations indicated a significant and positive relationship between depression severity and SNAQ-11 score. CRS with polyps was negatively associated with SNAQ-11 scores but, as expected, it was positively associated with a higher LMS. CONCLUSIONS: Lower SES status is a negative modifying factor of subjective CRS severity but it has no impact on the LMS. Depression symptoms are associated with increased subjective CRS severity but they have no effect on the LMS. How SES and depression impact on a patient`s self-reported disease severity requires further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".