Injection Augmentation of Type 1 Laryngeal Clefts
Bibliographic record
Abstract
OBJECTIVE: To describe a series of children diagnosed with type I congenital laryngeal clefts (LC-I) and treated, due to various presentations, with endoscopic injection augmentation (IA). STUDY DESIGN: Case series with chart review. SETTING: Tertiary care academic children's hospital in Edmonton, Canada. SUBJECTS: All pediatric patients diagnosed with LC-I and treated with IA in a single tertiary care practice. METHODS: The children were identified from a prospectively collected database. Only those who were treated with IA and had a minimum follow-up of 3 months were included. The authors collected demographics, diagnoses, surgical procedures, number of IA procedures, clinical outcomes, and complications. RESULTS: Over a period of 8 years, 43 patients were diagnosed with LC-I. Eighteen had undergone IA over the past 4 years. Mean age at IA was 37.11 ± 32.68 months with a male-to-female ratio of 1.25:1. The indications were swallowing dysfunction (13), atypical croup (2), chronic cough (1), cyanotic spells (1), and asthma (1). Seven patients required repeated injections (mean, 2.57 injections). A total of 13 patients responded with resolution of symptoms in question. A single postoperative complication was recorded. CONCLUSION: IA is a brief, simple management option that succeeds in a number of children with LC-I. It is minimally morbid and supplements other conservative approaches to treat the condition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".