What traditional neuropsychological assessment got wrong about mild traumatic brain injury. IV: clinical applications and future directions
Bibliographic record
Abstract
Primary objective Part IV concludes this four-part review of ‘What Traditional Neuropsychological Assessment Got Wrong About Mild Traumatic Brain Injury,’ with a focus on clinical applications and future directions.Methods and procedures These reviews have highlighted the limitations of traditional neuropsychological assessment methods, particularly in the evaluation of the patient with mild traumatic brain injury (mTBI), and especially within the context of all of the 21st Century advances in neuroimaging, quantification and network neuroscience.Main outcome and results How advanced neuroimaging technology and contemporary network neuroscience can be applied to assessing the mTBI patient at this time along with neuroimaging of the future are reviewed. The current status of computerized neuropsychological test (CNT) development is reviewed as it applies to mTBI assessment. Likewise, how the future of various types of virtual reality (VR), artificial intelligence (AI), wearable sensors, and markerless gaming technology could enhance the mTBI CNT assessment tool box of the future is reviewed.Conclusions The review concludes with some aspirational statements about how improvements along with novel CNT methods could be developed and integrated with advanced neuroimaging technologies in the future to be tailored to meet the needs of the mTBI patient.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".