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Record W7015545649

Stress and Immune Mediators In The Canadian Armed Forces: Association Between Basal Levels and Military Physical Performance

2019· article· en· W7015545649 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAssociation (psychology)Basal (medicine)Immune systemGrip strengthAerobic capacityAllostatic loadAttritionLinear regression
DOInot available

Abstract

fetched live from OpenAlex

Purpose: This study aimed to determine if an association exists between basal levels of stress and immune mediators and military physical performance among Canadian Armed Forces members (CAF).
\nMethods: Blood samples from 219 CAF members (86 women and 133 men), were analysed for levels of cortisol, C-reactive protein (CRP), Adiponectin, INF-γ, TNF-α, IL-1β, IL-2, IL-6, IL-8 and IL-18. Grip strength, aerobic capacity and performance on six military physical performance tests (sandbag fortification, escape to cover, picking and digging, picket and wire carry, stretcher carry and vehicle extrication) were also assessed. A composite score for total performance was computed, based on rank scores from the six tasks.
\nResults: The results from the linear regression analysis showed that higher CRP values were associated with lower total performance scores (slope -23.0,p≤0.05), a slower picking and digging time (slope 45.75,p≤0.05), lower aerobic capacity (slope -71.81,p≤0.05) and shorter plank time (slope -21.82,p≤0.05). A positive association between IL-2 values and grip strength was also observed (slope 20.83,p≤0.05). Adiponectin values were positively associated with plank time (slope 21.42,p≤0.05), but negatively associated with grip strength (slope -8.27,p≤0.05).
\nConclusion: The results from this study suggest that high levels of immune mediators (like CRP) could be a marker of decreased military physical performance.

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)0.000
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 score0.115
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.264
Teacher spread0.250 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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