Bibliographic record
Abstract
Abstract Traumatic brain injury, generally defined as an alteration in brain function, or other evidence of brain pathology as a result of an external force, has established itself as a significant threat to public health. It impacts survivors and their families, and demonstrates clear economic repercussions at the societal level, costing hundreds of billions of dollars annually worldwide. To detect, manage and prevent this complex and often lifelong disabling injury, it is essential to understand its distribution and patterns, and have comprehensive knowledge of persons at risk of injury and adverse outcomes. Key Concepts Traumatic brain injury (TBI) is on the rise worldwide. The lack of a universal definition of TBI presents a problem for quantification of true TBI burden. The baseline clinical severity measures for TBI are often based on symptomatology, loss of consciousness and post‐traumatic amnesia; discordance between clinical severity and evidence of injury based on neuroimaging is common; investment in biomarkers of neuronal and glial integrity is growing. Persons with similar injury severity and acute presentation can experience significantly different clinical course and functional outcomes. Certain groups of people are more vulnerable to TBI and adverse outcomes than others, owing to unique interactions between their biological, behavioural, social and cultural standing before and after the injury. Clinicians, researchers and policy leaders dealing with critical injuries with long‐lasting effects such as TBI cannot afford to leave its probability in the future to chance; greater investment in injury prevention is timely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.180 | 0.073 |
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".