Combat amputee care for Global War on Terror Veterans: A systematic review
Bibliographic record
Abstract
Introduction: Traumatic amputation after injuries sustained from combat during the Global War on Terror resulted in a multitude of clinical outcomes that included physical and psychological complications. The U.S. Department of Veterans Affairs and U.S. Department of Defense instituted the Advanced Rehabilitation Centers (ARCs) in 2007 to help address the growing amputee service member population. This article seeks to determine how the current literature describes physical, mental, and social health outcomes for U.S. combat amputee service members since the development of the ARCs. Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The following databases were searched for articles to screen: PubMed (NCBI), EMBASE (Elsevier), CINAHL Complete (EBSCOhost), and MEDLINE (EBSCOhost). The following key words were used: "traumatic amputation," "amputee," "Veteran," and "military" to find articles published between 2007 and March 2023. Results: Amputee service members are at increased risk of detrimental health outcomes when compared to non-amputee service members. The timing of amputation has a significant impact on short- and long-term health outcomes. Research trends were focused on pain and pain management, comparisons to Vietnam Veteran amputees, prosthesis satisfaction and functionality, limb salvage, quality of life and mental health, and general health outcomes. Discussion: The treatment of amputee Veterans requires special consideration between short-term complications, long-term health outcomes, and psychological diagnoses to help increase quality of life.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".