Effect of Serial Intralesional Steroid Injections on Risk of Recurrence in Idiopathic Subglottic Stenosis
Bibliographic record
Abstract
BACKGROUND: Serial intralesional steroid injections (ILSIs) have been suggested to be an effective adjunct treatment for idiopathic subglottic stenosis (iSGS) by maintaining airway patency and extending inter-surgical intervals. However, evidence for the effectiveness of serial ILSIs remains inconclusive. The current study aimed to assess whether ILSIs reduce the risk of subsequent surgical dilation (i.e., recurrence) in a cohort of patients with iSGS. METHODS: Prospectively collected clinical data for 75 female iSGS patients with 1-4 years of follow-up were analyzed. To assess the effect of ILSI use on the risk of recurrence, we assessed both the time-to-first recurrence using a standard Cox proportional hazards model and all recurrences per patient using a recurrent-events model. Overall, there were 36 patients who had received ILSIs at any point in the follow-up period and 39 patients who had not. RESULTS: ILSI use was associated with a significantly reduced risk of recurrence in both time-to-first event (hazard ratio (HR) = 0.20, 95% confidence interval (CI) 0.08-0.49) and recurrent events (HR = 0.44, 95% CI 0.26-0.75) multivariate Cox proportional hazard models, along with older age at diagnosis and longer time since diagnosis (all p < 0.05). In the time-to-first event analysis, the median time to recurrence among those who had received ILSIs was 2.5 years compared to 1.4 years among those who had not. The number needed to treat with ILSIs to avoid one recurrence by 2 years follow-up was two. CONCLUSION: Serial ILSIs were associated with reduced risk of recurrence, along with older age at diagnosis and longer time since diagnosis. LEVEL OF EVIDENCE: 3 (non-randomized controlled cohort/follow-up study).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".