Optimizing the Diagnosis and Management of Pediatric Inducible Laryngeal Obstruction
Bibliographic record
Abstract
BACKGROUND: Pediatric inducible laryngeal obstruction (ILO) is difficult to diagnose and treat. Patients often undergo multiple specialist referrals, and long-term outcomes are not well reported. OBJECTIVES: To investigate the patterns of presentation, workup, and management of children who were diagnosed with ILO at the Stollery Children's Hospital. METHODS: Retrospective review with a prospective cohort of pediatric patients diagnosed with ILO from 2015 to 2023. We collected the demographic data, diagnostic tests, specialist referrals, time to diagnosis, symptom burden, associated comorbidities and aggravating factors, management, and treatment outcomes. A subset of patients was followed prospectively to determine treatment outcomes. A basic descriptive analysis was performed, and factors associated with time to resolution were studied. RESULTS: Seventy-eight patients met the criteria for inclusion, with 22 completing prospective questionnaires. The average age was 14 years old, and 75% were female. The majority required multiple specialist referrals. The majority were associated with exercise. Thirty-two (41%) patients had a presumed diagnosis of asthma, despite only four pulmonary function tests being consistent with asthma. Abortive breathing exercises were the most commonly employed (95%) and most successful (61%) nonsurgical management technique. Surgery was highly successful in a small cohort of patients. Median time to symptom resolution was 12 months, with 36% reporting symptoms persistent beyond 3 years. CONCLUSIONS: Pediatric ILO often goes undiagnosed for prolonged periods. Exercise-related symptoms are the most common. Management strategies have varied levels of success and a large proportion of patients have prolonged symptoms despite treatment, as supported by other recent evidence. LEVEL OF EVIDENCE: 3 Laryngoscope, 135:1207-1211, 2025.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".