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Record W6884601738 · doi:10.1051/sm/2022007/pdf

A preliminary study on assessment of lead exposure in competitive biathletes: and its effects on respiratory health

2022· article· en· W6884601738 on OpenAlexaff

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2022
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsRespiratory systemLung functionLead (geology)SensitizationAsthmaProvocation testAirwayLead exposurePulmonary function testing

Abstract

fetched live from OpenAlex

Aim: In this preliminary study, we aimed to assess the blood lead level (BLL) in biathletes compared to cross-country skiers, and to look at the effects on airway function, responsiveness, allergic sensitization and the report of training-induced respiratory symptoms. Methods: Eleven biathletes (19 ± 2 years old, sex: 6M:4F) and 12 cross-country skiers (18 ± 3 years old, sex: 4M:8F) had a blood sample, spirometry, bronchial provocation test to Methacholine, skin prick tests, and induced sputum. Biathletes performed the tests within 3 h after a 90 to 120 min shooting session (150 ± 45 bullets fired). Results: Lung function, airway responsiveness, sensitization to common airborne allergens, and the report of training-induced respiratory symptoms were not different between both groups of winter sport athlete. BLL was significantly higher in biathletes vs. cross-country skiers (geometric mean [95%CI]: 2.15 [1.37–2.94] μg/dL vs. 0.85 [0.81–0.89] μg/dL, respectively, p < 0.001, Cohen’s d = 1.25). One biathlete had a BLL greater than the recommended threshold (> 5 μg/dL). Significant correlations were observed in biathletes only between BLL and FEV1 and FVC in absolute value (r = 0.69, p = 0.02 and r = 0.69, p = 0.02, respectively). Conclusion: Despite higher BLL in biathletes, no difference in atopy, respiratory function or symptoms was observed with cross-country skiers in our experimental conditions.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.389
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.322
Teacher spread0.291 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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