Development of a clinically relevant endoscopic grading system for chronic rhinosinusitis using canonical correlation analysis
Bibliographic record
Abstract
BACKGROUND: Diagnostic nasal endoscopy is a routine measure of sinonasal inflammation in patients with chronic rhinosinusitis (CRS). Although multiple staging systems have been proposed and evaluated, evidence of association between concurrent symptoms and endoscopic findings remains discordant. The goal of this study is to identify the relevant endoscopic attributes associated with symptom burden, and to systematically derive a weighted endoscopic scale that optimizes prediction of concurrent symptoms. METHODS: Reported baseline symptom (22-item Sino-Nasal Outcome Test [SNOT-22]) and endoscopic evaluation scores (Lund-Kennedy [LK]) were obtained from patients with CRS enrolled in a prospective cohort study. Canonical correlation analysis of the SNOT-22 subdomains and LK variables was completed. RESULTS: A total of 629 patients were included in analysis including 343 with prior endoscopic sinus surgery. Significant canonical correlations outperformed aggregate correlations in explaining variance of the data (33% vs 3%, respectively). The first canonical correlation was dominated by the rhinologic symptom domain and the endoscopic polyp score (r = 0.54; p < 0.05) whereas additional significant canonical correlation was found between the extra-rhinologic symptom subdomain and the edema score in patients without prior ESS (r = 0.21; p < 0.05), and discharge in patients with prior ESS (r = 0.22; p < 0.05). All other domains and endoscopic variables did not significantly contribute to the canonical correlation. CONCLUSION: Although aggregate symptoms and endoscopic scores demonstrate minimal correlation, a weighted combination of symptom domains and endoscopic attributes greatly improves this correlation. A simple approximation of the weights of each of the endoscopic variables of polyps, edema, discharge, scarring, and crusting, is an approximate ratio of 4:2:1:0:0, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".