Dilation versus laser resection in subglottic stenosis: protocol for a prospective international multicentre randomised controlled trial (AERATE trial)
Bibliographic record
Abstract
INTRODUCTION: Subglottic stenosis (SGS) is a rare condition that results from progressive narrowing of the upper airways. Outcomes and treatment options depend on the benign or complex nature of the stenosis. Treatment options for SGS include surgery and endoscopic techniques. The main endoscopic techniques used are dilation and laser resection. Observational and retrospective studies suggest that endoscopic laser resection may be more effective than dilation. We, therefore, aimed to compare the effectiveness of dilation and laser resection in preventing recurrence of SGS. METHODS AND ANALYSIS: AERATE (dilAtion vs laser Endoscopic Resection in subglottic trAcheal sTEnosis) is a multicentre, investigator-initiated, randomised controlled trial, comparing endoscopic dilation to endoscopic laser resection for simple benign SGS (less than 1 cm long without underlying cartilaginous damage) referred for endoscopic treatment (first treatment or recurrence). The study will be conducted in three centres in France and one in Canada with other centres from France and Canada expected to join. The primary outcome is the recurrence rate of stenosis at 2 years. Recurrence is defined as having a new onset of symptoms along with a stenosis of more than 40% (confirmed by bronchoscopy) requiring a new procedure. A sample size of 100 patients is calculated for the primary endpoint assuming a 10% recurrence rate in the laser resection group and 33% in the dilation group with a statistical significance level of 5%, a power of 80%. ETHICS AND DISSEMINATION: This study is approved by local and national ethics committees as required. Results will be published, and trial data will be made available. TRIAL REGISTRATION NUMBER: NCT04719845.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".