Prevalence and incidence of idiopathic subglottic stenosis in southern and central Alberta: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Subglottic stenosis (SGS) is a reportedly rare disease that causes recurrent severe airway obstruction. Etiologies reported for SGS include idiopathic, iatrogenic, autoimmune, congenital, and traumatic, with variable ratios among different centres. From empiric observation, southern and central Alberta was hypothesized to have a disproportionate distribution of SGS driven by increased idiopathic SGS (iSGS) compared to previous literature. Identification of causative agents of iSGS will help understand and guide future management options, so this study aimed to characterize the demographics of SGS subtypes, define prevalence and incidence rates of iSGS in southern Alberta, and geographically analyze for clustering of iSGS prevalence. METHODS: SGS patients from Alberta census divisions No. 1-9 and 15 were retrospectively reviewed. Patients were subtyped according to etiology of SGS and characterized. Idiopathic SGS prevalence and incidence was assessed; prevalence was further geographically segregated by census division and forward sortation area (FSA). Significant clustering patterns were assessed for using a Global Moran's I analysis. RESULTS: From 2010 to 2019 we identified 250 SGS patients, who were substantially overrepresented by idiopathic patients (80.4%) compared to autoimmune (10.0%), iatrogenic (7.6%), congenital (1.2%), and traumatic (0.8%). The total iSGS prevalence was 9.28/100,000 with a mean annual incidence rate of 0.71/100,000 per year. Significant clustering was observed (Moran's index 0.125; z-score 2.832; p = 0.0046) and the highest rates of prevalence were observed in southern Alberta and in rural communities heterogeneously dispersed around Calgary FSAs. CONCLUSION: In southern and central Alberta, iSGS patients were disproportionately over-represented in contrast to other subtypes with the highest prevalence in southern Alberta. There was a three-fold higher annual incidence compared to previous literature demonstrating the highest rates of disease reported worldwide. Future research aims to expand the geographical scope and to assess for demographic or environmental differences within significant clusters that may contribute to disease pathophysiology. LEVEL OF EVIDENCE: III.
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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 | 0.001 | 0.001 |
| 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".