EFNS guidelines on cognitive rehabilitation: report of an EFNS task force
Bibliographic record
Abstract
Disorders of language, spatial perception, attention, memory, calculation and praxis are a frequent consequence of acquired brain damage [in particular, stroke and traumatic brain injury (TBI)] and a major determinant of disability. The rehabilitation of aphasia and, more recently, of other cognitive disorders is an important area of neurological rehabilitation. We report here a review of the available evidence about effectiveness of cognitive rehabilitation. Given the limited number and generally low quality of randomized clinical trials (RCTs) in this area of therapeutic intervention, the Task Force considered, besides the available Cochrane reviews, evidence of lower classes which was critically analysed until a consensus was reached. In particular, we considered evidence from small group or single cases studies including an appropriate statistical evaluation of effect sizes. The general conclusion is that there is evidence to award a grade A, B or C recommendation to some forms of cognitive rehabilitation in patients with neuropsychological deficits in the post-acute stage after a focal brain lesion (stroke, TBI). These include aphasia therapy, rehabilitation of unilateral spatial neglect (ULN), attentional training in the post-acute stage after TBI, the use of electronic memory aids in memory disorders, and the treatment of apraxia with compensatory strategies. There is clearly a need for adequately designed studies in this area, which should take into account specific problems such as patient heterogeneity and treatment standardization.
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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 | 0.071 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.026 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".