A Global Perspective on Spinal Cord Injury Epidemiology
Bibliographic record
Abstract
Spinal cord injury (SCI) is a devastating condition often affecting young and healthy individuals around the world. This debilitating condition not only creates enormous physical and emotional cost to individuals but also is a significant financial burden to society at large. This review was undertaken to understand the global impact of SCI on society. We also attempted to summarize the worldwide demographics and preventative strategies for SCI in varying economic and climatic environments and to evaluate how cultural and economic differences affect the etiology of SCI. A PUBMED database search was performed in order to identify clinical epidemiological studies of SCI within the last decade. In addition, World Bank and World Health Organization websites were used to obtain demographics, economics, and health statistics of countries of interest. A total of 20 manuscripts were selected from 17 countries. We found that SCI varies in etiology, male-to-female ratios, age distributions, and complications in different countries. Nations with similar economies tend to have similar features and incidences in all the above categories. However, diverse methods of classifying SCI were found, making comparisons difficult. Based upon these findings, it is clear that the categorization and evaluation of SCI must be standardized. The authors suggest improved methods of reporting in the areas of etiology, neurological classification, and incidence of SCI so that, in the future, more useful global comprehensive studies and comparisons can be undertaken. Unified injury prevention programs should be implemented through methods involving the Internet and international organizations, targeting the different etiologies of SCI found in different countries.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".