The Incidence of Dental Disease Nonbattle Injuries in Deployed U.S. Army Personnel
Bibliographic record
Abstract
BACKGROUND: In the past, the U.S. Army Reserve (USAR) and Army National Guard (ARNG) have exhibited lower levels of medical and dental readiness than active duty (AD) Soldiers when activated for deployment. OBJECTIVE: The objective was to compare dental disease and nonbattle injury (D-DNBI) incidence rates and describe the most common D-DNBI diagnoses in Army AD, ARNG, and USAR Soldiers deployed to Iraq (Operation Iraqi Freedom/Operation New Dawn) and Afghanistan or Kuwait (Operation Enduring Freedom). METHODS: Data from the Center for AMEDD Strategic Studies (CASS) were used to determine D-DNBI encounter rates and diagnoses for deployed Army Soldiers. RESULTS: "Dental Caries" was the leading diagnosis (10.00%) for Soldiers in both theaters. For Operation Iraqi Freedom, D-DNBI rates were highest in 2010 at 144.05 per 1,000 Soldiers per year (AD 135.77, ARNG 151.39 and USAR 183.76). In comparison, D-DNBI rates in Operation Enduring Freedom were highest in 2012 with an overall rate of 85.77 per 1,000 Soldiers per year (AD 72.48, ARNG 129.38 and USAR 129.52). CONCLUSIONS: In both campaigns, the data suggest that ARNG and USAR Soldiers had higher D-DNBI rates when compared to AD Soldiers. Further investigation is needed to decrease D-DNBI rates and to determine risk factors that may influence D-DNBI rates among Army components during deployments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".