Retrospective analysis of idiopathic subglottic stenosis treatment in two Dutch Centers
Bibliographic record
Abstract
Introduction: Idiopathic subglottic stenosis (ISGS) is a rare condition with an estimated prevalence of 1:400,000. Pulmonary function tests (PFTs) typically reveal an obstructive pattern, characterized by reduced peak expiratory flow (PEF) and forced inspiratory flow (FIV1). While endoscopic treatments yield excellent initial results, recurrence rates reach 87% at five years. The standard approach involves incision and dilatation, often supplemented with triamcinolone injections, submucosal resection, or cryotherapy to improve outcomes. Aims and Objectives: This study evaluates the impact of initial endoscopic interventions on time to recurrence in ISGS patients and aims to identify strategies that prolong symptom-free intervals. Methods: A retrospective analysis was conducted on ISGS cases managed at two Dutch referral centers. Time to recurrence was defined as days to re-intervention. Patients were grouped by initial treatment: (1) incision and dilatation; (2) incision, dilatation, and triamcinolone; (3) multimodality treatment, including submucosal resection or cryotherapy. Results: Among 96 patients, no significant difference in time to recurrence was observed between treatment groups. Conclusion: Altough no statistical significance was reached, findings suggest multimodality treatment may help delay ISGS recurrence. Further prospective studies are needed to refine treatment protocols and improve long-term outcome. erj;66/suppl_69/PA6182/F1 F1 F1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".