The Neuropsychology of Traumatic Brain Injury: Looking Back, Peering Ahead
Bibliographic record
Abstract
The past 50 years have been a period of exciting progress in neuropsychological research on traumatic brain injury (TBI). Neuropsychologists and neuropsychological testing have played a critical role in these advances. This study looks back at three major scientific advances in research on TBI that have been critical in pushing the field forward over the past several decades: The advent of modern neuroimaging; the recognition of the importance of non-injury factors in determining recovery from TBI; and the growth of cognitive rehabilitation. Thanks to these advances, we now have a better understanding of the pathophysiology of TBI and how recovery from the injury is also shaped by pre-injury, comorbid, and contextual factors, and we also have increasing evidence that active interventions, including cognitive rehabilitation, can help to promote better outcomes. The study also peers ahead to discern two important directions that seem destined to influence research on TBI over the next 50 years: the development of large, multi-site observational studies and randomized controlled trials, bolstered by international research consortia and the adoption of common data elements; and attempts to translate research into health care and health policy by the application of rigorous methods drawn from implementation science. Future research shaped by these trends should provide critical evidence regarding the outcomes of TBI and its treatment, and should help to disseminate and implement the knowledge gained from research to the betterment of the quality of life of persons with TBI. (JINS, 2017, 23, 806-817).
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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 | 0.042 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".