In Response to <i>Optimizing the Diagnosis and Management of Pediatric Inducible Laryngeal Obstruction</i>
Bibliographic record
Abstract
The Letter to the Editor in reference to Optimizing the Diagnosis and Management of Pediatric Inducible Laryngeal Obstruction makes some helpful and important points.1 The author of the letter appropriately clarifies that inducible laryngeal obstruction (ILO) and vocal cord dysfunction (VCD) are not synonymous terms. We agree with this and did not intend to imply this in the introductory section of the manuscript. We aimed to make the point that these terms are often inappropriately used interchangeably and that many studies that claimed to report on VCD are actually referring to the wider disease of ILO and vice versa. The term ILO was more appropriate for the population under study in the current manuscript as some patients did not have obstruction or inappropriate adduction at the vocal cords only. The author of the letter raised concerns about the use of exercise laryngoscopy as a valid means to identify the etiology of laryngeal obstruction. We agree that the differential of this problem involves organs and diseases outside the larynx and did not imply that exercise laryngoscopy can diagnose all such diseases. However, several studies have demonstrated the utility of exercise laryngoscopy2, 3 and it is part of the European consensus statement on ILO.4 When interpreted in the context of symptoms and the results of other investigations, it is a helpful diagnostic tool. Moreover, if symptoms are present primarily during exercise and there is suspicion of a laryngeal cause, it makes common sense to visualize the larynx during exercise while the patient is reporting to have symptoms.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".