Toward an International Initiative for Traumatic Brain Injury Research
Bibliographic record
Abstract
The European Commission (EC) and the National Institutes of Health (NIH) jointly sponsored a workshop on October 18-20, 2011 in Brussels to discuss the feasibility and benefits of an international collaboration in the field of traumatic brain injury (TBI) research. The workshop brought together scientists, clinicians, patients, and industry representatives from around the globe as well as funding agencies from the EU, Spain, the United States, and Canada. Sessions tackled both the possible goals and governance of a future initiative and the scientific questions that would most benefit from an integrated international effort: how to optimize data collection and sharing; injury classification; outcome measures; clinical study design; and statistical analysis. There was a clear consensus that increased dialogue and coordination of research at an international level would be beneficial for advancing TBI research, treatment, and care. To this end, the EC, the NIH, and the Canadian Institutes of Health Research expressed interest in developing a framework for an international initiative for TBI Research (InTBIR). The workshop participants recommended that InTBIR initially focus on collecting, standardizing, and sharing clinical TBI data for comparative effectiveness research, which will ultimately result in better management and treatments for TBI.
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 | 0.313 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.013 | 0.027 |
| 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".