A Perspective From the Middle East on the Topic of Concussion
Bibliographic record
Abstract
Background: Concussion is the most prevalent form of traumatic brain injury. Western countries debate it as a public health issue. Middle Eastern (ME) countries lag behind with a concussion incidence surveillance of 25% that of European countries. Objective: The purpose of our study was to review concussion resulting from traumatic brain or sports injuries in civilian nationals of the ME. Methods: We carried out PubMed literature search of all related articles in the past thirty years using search terms reflecting concussion and sports injuries in ME countries of Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Turkey, United Arab Emirates, and Yemen. Results: 72 articles met our search criteria with relatively little data found on concussion within the parameters of this review. However, the reports that were found were diverse. Israel, Turkey and Iran led in publications. Motor vehicle accidents were the leading cause of concussion from TBI (50-57%) followed by domestic injuries (30-40%) and sports injuries at 4-7%. Extremity injuries were most commonly reported unlike head injuries often invisible and underreported. Male gender, young children, pedestrian and car traffic accidents, lack of protective gear, cell phone use, impulsive behaviors as well as training overload, lack of sleep, contact sports and violence were all risk factors for concussion. Conclusions: In this review, we highlighted the nascent topic of concussion in the ME and the need for additional research dictating awareness programs and implementing new safety policies to lower morbidity and mortality across all ages.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".