Socioeconomic, comorbidity, lifestyle and quality of life comparisons between chronic rhinosinusitis phenotypes: Data from the National Chronic Rhinosinusitis Epidemiology Study
Bibliographic record
Abstract
Background: Chronic rhinosinusitis (CRS) is a heterogenous group of inflammatory sinonasal disorders with key defining symptoms, but traditionally separated into phenotypes by clinical/endoscopic findings. It is not known if the two phenotypes have differing socioeconomic, co-morbidity and lifestyle differences. Objective: This analysis of the Chronic Rhinosinusitis Epidemiology Study (CRES) database sought to analyse any key differences in the socioeconomic variables between those with CRS with nasal polyps (CRSwNPs) and those without nasal polyps (CRSsNP). We also sought to analyse differences in comorbidities, lifestyle and quality of life. Methods: Patients with a confirmed diagnosis of CRS in secondary and tertiary care outpatient settings were invited to participate in a questionnaire based case-control study. Variables included demographics, comorbidities, socioeconomic factors, lifestyle factors and health related quality of life. Results: A total of 1204 patients’ data were analysed; 553 CRSsNP and 651 CRSwNP participants. The key socioeconomic variables did not demonstrate any notable differences, nor did lifestyle variables other than alcohol consumption being higher in those with CRSwNP (p=0.032). Aside from confirmation of asthma being more common in CRSwNP, it was notable that this group complained less of URTIs and CRSsNP participants showed evidence of lower HRQoL scores in respect of body pain (p=0.001). Conclusions: Patients with CRSwNP experience higher rates of asthma and lower rates of URTIs but otherwise do not demonstrate significant socioeconomic, comorbidity, lifestyle or quality of life issues other than for body pain and alcohol consumption.
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 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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".