Are Questionnaires on Respiratory Symptoms Reliable Predictors of Airway Hyperresponsiveness in Athletes and Sedentary Subjects?
Bibliographic record
Abstract
This study aimed at determining the frequency of respiratory symptoms in high-level athletes and whether respiratory questionnaires are reliable predictors of airway hyperresponsiveness (AHR) in this population compared with control subjects. One hundred high-level athletes exercising in different conditions of ambient air (dry, humid, cold or mixed dry and humid) and 50 sedentary control subjects answered four question sets on exercise-induced symptoms of postnasal drip (Q1), breathlessness, chest tightness and wheezing (Q2), and cough (Q3). Another question set (Q4) evaluated the self-description of nociceptive sensations associated with respiratory symptoms. Methacholine inhalation tests were performed in all subjects to obtain a 20% fall in forced expiratory volume in 1 second (PC20). AHR could be detected by questionnaires in 37 of 44 (84%) subjects with a PC20 < 8 mg/mL. Sensitivity to detect AHR varied between the different subgroups of athletes with each of the question sets; however, no significant differences in sensitivity were observed between the groups of athletes and controls except for Q3 (P=.007), in which athletes exercising in cold air reported more exercise-induced cough. Q2 had a better specificity (83%) than Q3 (77%) and Q4 (64%). Combined question sets revealed that three swimmers, two triathletes, and two controls, who answered negatively to all question sets, had a PC20 < 8 mg/mL. Questionnaires on symptoms and on associated nociceptive sensations may help to detect AHR as well in athletes and controls, although for some subgroups of athletes such as swimmers and triathletes and in some controls, false negative questionnaires can be observed and AHR underreported.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".