What traditional neuropsychological assessment got wrong about mild traumatic brain injury. I: historical perspective, contemporary neuroimaging overview and neuropathology update
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This is Part I of a four-part review that examines traditional neuropsychological assessment methods and techniques in mild traumatic brain injury (mTBI). Absence of neuropsychological findings has been used to argue no residual neuropathological effects from mTBI. However, given the current potential that advanced multimodality and quantitative neuroimaging can now demonstrate about underlying neurobiology of brain-behavior relations, this review shows that traditional neuropsychological test as standalone findings cannot directly address the underlying complexities of detecting mTBI neuropathology. RESEARCH DESIGN: This is a review. METHODS AND PROCEDURES: century advanced neuroimaging and improved understanding of the neurobiology and potential neuropathology of mTBI. MAIN OUTCOME AND RESULTS: Traditional neuropsychological methods were never developed for detecting subtle changes in neurocognitive or neurobehavioral functioning as a standalone procedure and likewise, never designed to address the multifaceted issues related to symptom burden from having sustained a mTBI, especially after three-months post-injury. Advanced neuroimaging methods have the potential to inform the clinician and researcher about potential neurobiological factors to best understand relevant neuropsychological outcome factors associated with mTBI outcome. A model is presented that helps explain how adaptation and accommodation may occur after mTBI within the context of 'normal' traditional neuropsychological test findings. CONCLUSIONS: The limitations of traditional neuropsychological testing in mTBI outlined within the context of how advanced neuroimaging improves our understanding of mTBI outcome.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".