INCOG Recommendations for Management of Cognition Following Traumatic Brain Injury, Part IV
Bibliographic record
Abstract
INTRODUCTION: Cognitive-communication disorders are common in individuals with traumatic brain injury (TBI) and can have a major impact on long-term outcome. Guidelines for evidence-informed rehabilitation are needed, thus an international group of researchers and clinicians (known as INCOG) convened to develop recommendations for assessment and intervention. METHODS: An expert panel met to select appropriate recommendations for assessment and treatment of cognitive-communication disorders based on available literature. To promote implementation, the team developed decision algorithms incorporating the recommendations, based on inclusion and exclusion criteria of published trials, and then prioritized recommendations for implementation and developed audit criteria to evaluate adherence to best practice recommendations. RESULTS: Rehabilitation of individuals with cognitive-communication disorders should consider premorbid communication status; be individualized to the person's needs, goals, and skills; provide training in use of assistive technology where appropriate; include training of communication partners; and occur in context to minimize the need for generalization. Evidence supports treatment of social communication problems in a group format. CONCLUSION: There is strong evidence for person-centered treatment of cognitive-communication disorders and use of instructional strategies such as errorless learning, metacognitive strategy training, and group treatment. Future studies should include tests of alternative service delivery models and development of participation-level outcome measures.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".