Bringing attention into higher focus within the traumatic brain injury research agenda.
Bibliographic record
Abstract
Traumatic brain injury (TBI) holds a unique position within children’s health. It has been clear for decades that TBI is the leading cause of death and disability of children (1,2). However, only recently has the impact of TBI on developing brain gained the attention of the public and lay press due to the attention paid toward injuries in sports such as football, boxing and others. Guidelines for caring for children with mild (3-5) and severe (6) injuries have been assiduously developed from the available literature, yet the proven therapies have remained elusive. Recently, the National Institute of Neurological Disorders and Stroke (NINDS), the European Commission and the Canadian Institutes of Health Research have led (and funded) efforts to address the burden of TBI with the International Initiative for Traumatic Brain Injury Research (InTBIR) with the goal of “working together to improve outcomes and lessen the global burden of TBI by 2020” (7).
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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".