Real‐world characterisation of patients with chronic rhinosinusitis with nasal polyps with and without surgery in England
Bibliographic record
Abstract
OBJECTIVES: To characterise the real-world burden of chronic rhinosinusitis with nasal polyps (CRSwNP) in the UK, stratified by number of surgeries. DESIGN: Retrospective cohort study. SETTING: UK Clinical Practice Research Datalink Aurum database with Hospital Episodes Statistics linkage (2007-2019). PARTICIPANTS: Adults ≥18 years of age with a first NP diagnosis (index) and 365 days of baseline and ≥180 days of follow-up data. Follow-up continued until disenrollment, death or end of data collection. MAIN OUTCOME MEASURES: Primary: primary care physician prescribed CRSwNP-related treatments, and all-cause healthcare resource utilisation (HCRU) in 90 days post-index, stratified by surgeries during follow-up. Secondary: rate of surgery and CRSwNP point prevalence. Baseline patient demographics, clinical characteristics and comorbidities were also assessed. RESULTS: Of the 33 107 patients included, 23.5% and 2.2% had ≥1 and ≥2 surgeries during follow-up, respectively (mean follow-up: 5.3 years). Patients with more surgeries (≥2/≥1/0) during follow-up were more likely to be male (67.3%/69.0%/58.0%), have asthma (37.8%/28.2%/20.2%) and have baseline blood eosinophil counts ≥300 cells/μL (68.5%/66.0%/51.5%). During the first 90-days post-index as surgery number increased, the proportion of patients using oral corticosteroids (25.8%/20.7%/14.2%) and mean (SD) number of all-cause healthcare visits (5.9 [4.2]/5.4 [4.0]/4.9 [4.2]) increased. Time between surgeries was shorter among patients with more surgeries. CRSwNP prevalence on 31 December 2018 was 476 cases per 100 000 persons. CONCLUSION: A small proportion of patients in the UK required multiple surgeries for CRSwNP and this was associated with increasing comorbidity burden, baseline blood eosinophil counts, CRSwNP-related treatment and HCRU use.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".