INCOG Recommendations for Management of Cognition Following Traumatic Brain Injury, Part V
Bibliographic record
Abstract
INTRODUCTION: Traumatic brain injury results in complex cognitive sequelae. Impairments in memory are among the most common sequelae resulting in significant functional problems. An international team of researchers and clinicians (known as INCOG) was formed to develop recommendations for the management of impairments in memory. METHODS: The experts met to select appropriate recommendations and then reviewed available literature to ensure recommendations were current. Decision algorithms incorporating the recommendations based on inclusion and exclusion criteria of published trials were developed. The team then prioritized recommendations for implementation and developed audit criteria to evaluate adherence to the best practice recommendations. RESULTS: The recommendations for rehabilitation of memory impairments support the integration of internal and external compensatory strategies implemented using appropriate instructional techniques that consider functional relevance and important patient characteristics. Restorative strategies have regained significant popularity, given broader access to computer technology; however, evidence for efficacy of these techniques remains weak and the choice in using these should be guided by special considerations. CONCLUSION: There is good evidence for the integration of internal and external compensatory memory strategies that are implemented using instructional procedures for rehabilitation for memory impairments. The evidence for the efficacy of restorative strategies currently remains weak.
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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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".