A NATO Guide for Assessing Deployability for Military Personnel With Chronic Medical Conditions: Medical Fitness for Expeditionary Missions, Task Group 174, Human Factors, and Medicine Panel
Bibliographic record
Abstract
BACKGROUND: Each time a deployed military member has an exacerbation of a pre-existing chronic disease there is a potential risk to mission success, individual health, and the safety of the unit. Currently, North Atlantic Treaty Organization (NATO) member nations employ different approaches to assessing an individual's medical fitness for deployment. OBJECTIVE: To set the minimum medical standards for NATO deployments. METHODS: A seven nation task group met periodically from 2008 to 2012 to develop guidelines for frontline military physicians to assess medical fitness for deployment. RESULTS: A medical deployment guide for 31 specific diseases/conditions using a rational, standardized and algorithmic approach based on a red-yellow-green risk stratification. CONCLUSIONS: If adopted as a NATO policy, this guide could then be kept up-to-date through a process that allows nations to track individuals with known chronic disease who were deployed into a theater of operations, allowing the guide to become increasingly evidence-based, and also more accurate in quantifying the risk of exacerbation based on individual and disease characteristics, as well as the nature and length of the deployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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 source (direct Gemma or distilled Codex), 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".