Determination of the prevalence of bronchial asthma (BA) and development of an effective protocol to detect BA in japanese elite athletes
Bibliographic record
Abstract
Background The prevalence of bronchial asthma (BA) including exercise-induced bronchoconstriction (EIB) and airway hyperresponsivenss (AHR) is increasing not only among normal population but also among athletes and prevention of these attacks are just as life saving and crucial for the athlete. The negative impact on athletic performance and the effect of inhaled corticosteroids and ƒÀ2-agonists are factors to providing prevention strategies in these athletes. Objective To determine the prevalence of BA (including silent BA) and develop an effective protocol to detect BA and provide preventive measures in elite athletes. Methods Asthma questionnaires and spirometry were conducted for all regardless of their clinical history. Bronchodilator tests were administered to those who met our criteria of past history, suspicious symptoms, values of FEV1%, and concave shape of flow-volume curves. Bronchial provocation tests were held to detect EIB and AHR in some athletes with suspicious symptom. IOC-criteria were used to consider those tests to be positive. Diagnosis of BA was made by consideration of all factors. Participants 347 male and 305 female Japanese elite athletes participating at the Beijing Olympic Games 2009 and the Vancouver Olympic Games 2010. Main outcome measurements Detecting BA and silent BA is important to prevent BA attacks and begin early intervention strategies in elite athletes in view of their performance and doping violence. Results 21 athletes met IOC criteria in bronchodilator test. The provocation test was positive for asthma in four cases. The prevalence of BA among Japanese elite athletes was 12.1%. Number of Asymptomatic athletes diagnosed as BA reached to 2.9%. Conclusion The prevalence of BA among Japanese elite athletes is similar to prevalence of athletes reported and our protocol can sufficiently detect BA and silent BA as well.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".