The Incidence of Dental Disease Nonbattle Injuries in Deployed U.S. Army Personnel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it