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Record W4409035353 · doi:10.1097/jom.0000000000003381

The Consideration of Body Mass Index in Conjunction With VO2max Is Essential When Evaluating the Health and Wellness of Frontline Fire Suppression Personnel

2025· article· en· W4409035353 on OpenAlexaff
Loren Yavelberg, Hubbard Cody, G. Norman, Jamnik Veronica

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsOvarian Cancer CanadaYork UniversityUniversity of Toronto
Fundersnot available
KeywordsBody mass indexLean body massIndex (typography)MedicinePhysical fitnessVO2 maxGerontologyPhysical therapyBody weightComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: . The purpose of this investigation was to examine the Physical Employment Standards Pass Rates (PR) among different BMI and job-related aerobic fitness (VO 2 max) categories. METHODS: Researchers examined the relationship between BMI, job-related maximum oxygen consumption (VO 2 max), and PES PR among 1,992 male and female frontline fire suppression (FFS) candidates who completed the Physical Employment Standards assessment, which qualifies as a bona fide occupational requirement. RESULTS: No relationship between BMI and PR was observed; however, there was a significant relationship between VO 2 max and %PR and between BMI coupled with VO 2 max and %PR. This suggests that BMI should be considered in conjunction with VO 2 max when evaluating the health and wellness of FFS workers. CONCLUSIONS: Wellness and training programs for FFS workers should prioritize VO 2 max, musculoskeletal fitness, and maintaining or improving lean mass. This applies to various emergency-related, physically demanding occupations.

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 score0.018
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.409
Teacher spread0.369 · 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
Published2025
Admission routes1
Has abstractyes

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