INCOG 2.0 Guidelines for Cognitive Rehabilitation Following Traumatic Brain Injury, Part II: Attention and Information Processing Speed
Bibliographic record
Abstract
INTRODUCTION: Moderate to severe traumatic brain injury (MS-TBI) commonly causes disruption in aspects of attention due to its diffuse nature and injury to frontotemporal and midbrain reticular activating systems. Attentional impairments are a common focus of cognitive rehabilitation, and increased awareness of evidence is needed to facilitate informed clinical practice. METHODS: An expert panel of clinicians/researchers (known as INCOG) reviewed evidence published from 2014 and developed updated guidelines for the management of attention in adults, as well as a decision-making algorithm, and an audit tool for review of clinical practice. RESULTS: This update incorporated 27 studies and made 11 recommendations. Two new recommendations regarding transcranial stimulation and an herbal supplement were made. Five were updated from INCOG 2014 and 4 were unchanged. The team recommends screening for and addressing factors contributing to attentional problems, including hearing, vision, fatigue, sleep-wake disturbance, anxiety, depression, pain, substance use, and medication. Metacognitive strategy training focused on everyday activities is recommended for individuals with mild-moderate attentional impairments. Practice on de-contextualized computer-based attentional tasks is not recommended because of lack of evidence of generalization, but direct training on everyday tasks, including dual tasks or dealing with background noise, may lead to gains for performance of those tasks. Potential usefulness of environmental modifications is also discussed. There is insufficient evidence to support mindfulness-based meditation, periodic alerting, or noninvasive brain stimulation for alleviating attentional impairments. Of pharmacological interventions, methylphenidate is recommended to improve information processing speed. Amantadine may facilitate arousal in comatose or vegetative patients but does not enhance performance on attentional measures over the longer term. The antioxidant Chinese herbal supplement MLC901 (NeuroAiD IITM) may enhance selective attention in individuals with mild-moderate TBI. CONCLUSION: Evidence for interventions to improve attention after TBI is slowly growing. However, more controlled trials are needed, especially evaluating behavioral or nonpharmacological interventions for attention.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".