Eucapnic voluntary hyperpnoea and exercise-induced vocal cord dysfunction
Bibliographic record
Abstract
INTRODUCTION: Exercise-induced bronchoconstriction (EIB) is a common condition in endurance athletes. Exercise-induced vocal cord dysfunction (EIVCD) is a frequent confounder of EIB. The diagnosis of EIVCD may be challenging and can be missed as the problem is often intermittent and may only occur during intense exercise. Eucapnic voluntary hyperventilation (EVH) is the best test to detect EIB. This pilot study aimed to assess if EVH could be helpful in the diagnosis of EIVCD associated or not to EIB in athletes. METHODS: A nasolaryngoscopy was performed during a 6 min EVH test, in 13 female athletes suspected to have VCD, aged 21±7 years. Image analysis was conducted by two Ear Nose and Throat surgeons in random order. RESULTS: During the EVH, three athletes showed incomplete paradoxical vocal cords movement, without inspiratory stridor. However, 12 athletes showed marked supraglottic movement without inspiratory stridor. In two athletes, this supraglottic movement was severe, one showing a marked collapse of the epiglottis with an almost complete obstruction of the larynx by the arytenoid cartilage mucosa. In 3 of the 12 athletes with supraglottic movement, severe vibration of the mucosa covering the arytenoid cartilages was also observed. CONCLUSIONS: EVH challenge in athletes can provide information on various types of glottic and supraglottic obstruction in reproducing laryngeal movements during hyperventilation. Our findings make us suggest that exercise induced upper airway obstructions should be named: Exercise-induced laryngeal obstruction (EILO). Then, EILO should be divided in three categories: supraglottic, glottic (EIVCD) and mixed (glottic and supraglottic) obstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".