Bibliographic record
Abstract
The trauma pandemic disproportionately kills and maims citizens of low-income countries although the immediate cause of the trauma is often an industrial export of a high-income country, such as a motor vehicle. Addressing the trauma pandemic in low-income countries requires access to relevant research information regarding prevention and treatment of injuries. Such information is also generally produced in high-income countries. We reviewed two years' worth of articles from leading orthopaedic and general medical journals to determine whether the scientific literature appropriately reflects the global burden of musculoskeletal disease, particularly that due to trauma. General medical journals underrepresented musculoskeletal disease, but within musculoskeletal disease an appropriate majority of papers were regarding trauma, in particular the epidemiology and prevention of injury. Orthopaedic journals, while focusing on musculoskeletal conditions, substantially underrepresented the global burden of disease due to trauma and hardly consider injury epidemiology and prevention. If orthopaedic surgeons want to maximize their global impact, they should focus on writing about trauma questions relevant to their colleagues in low-income countries and ensuring these same colleagues have access to the literature.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".