Summary of the findings of the International Collaboration on Mild Traumatic Brain Injury Prognosis
Bibliographic record
Abstract
In 2004, the WHO Collaborating Centre for Neurotrauma, Prevention, Management and Rehabilitation Task Force published the first large systematic review and best evidence synthesis on the clinical course and prognosis for recovery after MTBI. Ten years later, the International Collaboration on Mild Traumatic Brain Injury Prognosis (ICoMP) formed to update the original WHO Task Force results. This summary review highlights important clinical findings from the full ICoMP results including the current evidence on the course and prognosis of recovery after MTBI in diverse patient populations (e.g., adults, athletes and children) and injury environments (e.g., motor vehicle collisions) as well as on the risk of long-term outcomes after MTBI, such as Parkinson's disease and dementia. Additional clinical areas of interest in MTBI are also discussed including the similarities between MTBI and other traumatic injuries and the risk of Second Impact Syndrome after sport concussion. Clinicians can use this information to help inform patients on the likely course of recovery after MTBI/concussion and guide better decision-making in the care of these patients.
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 | 0.049 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".