Significance of Dermatologic Findings in a Cohort of Depleted Uranium–Exposed Veterans of Iraqi Conflicts
Bibliographic record
Abstract
BACKGROUND: Depleted uranium (DU)-containing weapons have been used in military operations since 1991. There is interest in following veterans who were occupationally exposed to DU by either inhalation or retention of fragments. A cohort of DU-exposed Gulf War I veterans has been followed longitudinally at the Baltimore Veterans Administration Medical Center since 1993. OBJECTIVE: The aim was to monitor chronic dermatological findings associated with occupational DU exposure in the 2013 cohort. METHODS: Thirty-five veterans were evaluated. This study was reviewed and approved by the institutional review board and the Baltimore Veterans Administration Medical Center research service. Depleted uranium exposure was measured using creatinine-adjusted urine uranium concentrations (micrograms of uranium per gram of creatinine [μgU/gCrea]). Detailed medical histories, physical examinations, and exposure histories were performed. RESULTS: Using a cutoff level of 0.1 μgU/gCrea, 11 veterans were placed in the high-uranium exposure group, whereas 23 veterans were placed in the low-uranium exposure group. Retained fragments were documented in 91% of the high-exposure group versus that in 13% of the low-exposure group (P < 0.001), and fragment-related scarring was significantly increased in the high-exposure group (P = 0.002). Other dermatological findings such as dermatitis were also assessed. CONCLUSIONS: Fragment retainment and related scarring was significantly increased in veterans exposed to high levels of DU. Continuous monitoring of this cohort will yield interesting dermatological findings related to DU exposure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".