Assessing Cardiovascular Fitness on Military Recruitment
Bibliographic record
Abstract
Background: Military recruitment demands optimal health, with cardiovascular fitness being a key criterion. To assess candidates, military organizations worldwide employ standardized screening protocols. Initial evaluations typically involve history-taking and physical examinations based on guidelines from the American Heart Association and the European Society of Cardiology. Method: Electrocardiography (ECG) serves as an accessible and cost-effective screening tool. Abnormal findings in these initial tests necessitate further assessments to determine a candidate’s fitness for service. Depending on the severity and context, additional tests such as echocardiography or, in rare cases, coronary angiography may be conducted. However, cost constraints influence the extent of these evaluations in some countries. Aim: This article examines cardiovascular screening in military recruitment and the variations in assessment practices across different nations. -- Highlights: 1. This article addresses the importance of standardized yet flexible cardiovascular assessments essential for military screenings. While standardized protocols are essential for consistency, military screenings should also adapt to environmental factors, individual differences, and evolving fitness benchmarks to ensure accurate evaluations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".