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Record W1503965259 · doi:10.1111/sms.12527

Airway inflammation, cough and athlete quality of life in elite female cross‐country skiers: A longitudinal study

2015· article· en· W1503965259 on OpenAlexafffund
Michael D. Kennedy, Warren J. Davidson, Lisa E Wong, Suzanne Traves, Richard Leigh, Neil D. Eves

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2015
Typearticle
Languageen
FieldMedicine
TopicRespiratory and Cough-Related Research
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInterior HealthUniversity of CalgaryUniversity of Alberta
FundersMichael Smith Health Research BC
KeywordsMedicineSputumPhysical therapyInflammationQuality of life (healthcare)AirwayAthletesInternal medicineSurgeryPathology

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the effect of a season of cross-country training and racing on airway inflammation, cough symptoms, and athlete quality of life in female skiers. Eighteen elite female skiers performed sputum induction and completed the Leicester Cough Questionnaire (LCQ) and the Recovery-Stress Questionnaire (REST-Q) at three time points (T1 - May/Jun, T2 - Oct/Nov, T3 - Jan-Mar) during the year. No changes were observed between T1 and T2. However, an increase in sputum eosinophils and lymphocytes (P < 0.05) and a significant change in all three domains of the LCQ were observed between T1 and T3 (P < 0.05). A significant association was found between the total yearly hours of training and the change in the total cell count (r(2) = 0.74; P = 0.006), and a number of other sputum cell counts between T1 and T3. No changes were observed for any domain of the REST-Q. The results of this study demonstrate that airway inflammation and cough symptoms are significantly increased in elite female cross-country skiers across a year of training and racing. The increase in airway inflammation is related to the total amount of training and is worse during the winter months when athletes are training and racing in cold, dry air.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.407
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2015
Admission routes2
Has abstractyes

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