Injection augmentation and endoscopic repair of type 1 laryngeal clefts: Development of a management algorithm
Bibliographic record
Abstract
OBJECTIVES: To describe indications for injection augmentation (IA), endoscopic repair (ER) and conservative methods for the management of type 1 laryngeal cleft (LC1) and propose a management algorithm. We also aimed to compare success of IA and ER and determine independent predictors of treatment failure. METHODS: Retrospective study of patients diagnosed with LC1 at a Pediatric Otolaryngology referral centre between 2004 and 2016. All had pre-operative instrumental swallowing evaluation (VFSS/FEES), and were managed with a combination of conservative measures, IA and/or ER. We collected demographics, symptoms, comorbidities, VFSS/FEES results, and operative details. The primary outcome was symptom resolution by parental report. The secondary outcome was predictors of treatment failure. RESULTS: 88 patients were included in the analysis, with mean age 26 ± 25 months. Most presented with choking events (68%) or recurrent pneumonias (48%). In total, there were 55 IA performed and 45 ER. Of the patients who received IA, 19 required subsequent ER. 95% had symptom improvement, 67% had complete resolution. IA had a 56% long-term success rate, whereas that for ER was 85%. Tube feeding at initial evaluation was an independent predictor of treatment failure (HR 11.33 [1.51-84.97], p = 0.018). CONCLUSIONS: LC1 can be effectively managed with a combination of IA and ER with favorable results. Failure to respond to IA does not preclude ER, and both have their role in management. Patients who are tube fed have a higher probability of treatment failure. We propose a management algorithm that includes reasoning for conservative approaches, and reduces exposure to general anesthesia.
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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) | 0.000 | 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".