The Health Economics of the spinal cord injury or disease among veterans of war: A systematic review
Bibliographic record
Abstract
CONTEXT: Information on health-care utilization and the economic burden of disease are essential to understanding service demands, service accessibility, and practice patterns. This information may also be used to enhance the quality of care through altered resource allocation. Thus, a systematic review of literature on the economic impact of caring for SCI/D veterans would be of great value. OBJECTIVE: To systematically review and critically appraise the literature on the economics of the management of veterans with SCI/D. METHODS: Medline, EMBASE and PsycINFO databases were searched for articles on economic impact of management of SCI/D veterans, published from 1946 to September/2016. The STROBE statement was used to determine publication quality. RESULTS: The search identified 1,573 publications of which 13 articles fulfilled the inclusion/exclusion criteria with 12 articles focused on costs of management of SCI/D veterans; and, one cost-effectiveness analysis. Overall, the health care costs for the management of SCI/D veterans are substantial ($30,770 to $62,563 in 2016 USD per year) and, generally, greater than the costs of caring for patients with other chronic diseases. The most significant determinants of the higher total health-care costs are cervical level injury, complete injury, time period (i.e. first year post-injury and end-of-life year), and presence of pressure ulcers. CONCLUSIONS: There is growing evidence for the economic burden of SCI/D and its determinants among veterans, whereas there is a paucity of comparative studies on interventions including cost-effectiveness analyses. Further investigations are needed to fulfill significant knowledge gaps on the economics of caring for veterans with SCI/D.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".