Identifying mild traumatic brain injury in the post-acute polytrauma setting: a scoping review of diagnostic approaches and screening tools
Bibliographic record
Abstract
OBJECTIVE: Mild traumatic brain injury (mTBI) is frequently overlooked in polytrauma patients due to the overshadowing of more severe injuries, a fact that makes its identification in post-acute settings challenging since symptoms overlap with other conditions and no validated diagnostic tools exist. To address this gap, this scoping review explored the literature on mTBI diagnosis in post-acute civilian polytrauma settings. METHODS: By utilizing the Arksey and O'Malley framework and PRISMA-ScR guidelines, the review focused on studies from 2010 to 2024 related to delayed mTBI diagnosis in adults. Of the 696 studies identified, only six met the inclusion criteria, highlighting the limited research in this area. RESULTS: The review assessed various diagnostic tools including the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), neuropsychological tests, advanced imaging, and oculomotor assessments. However, these tools are limited in their ability to confirm whether an mTBI has occurred. The American Congress of Rehabilitation Medicine's updated mTBI criteria may offer the best diagnostic potential but require validation. CONCLUSION: According to the findings, there is a significant gap in validated diagnostic tools for mTBI in post-acute settings, which may negatively affect patient outcomes. Developing and validating effective screening tools for mTBI in the post-acute polytrauma setting should be the priority of future research in this area.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".