Review of clinical trials on the effectiveness of cognitive rehabilitation in patients with traumatic brain injury
Bibliographic record
Abstract
Introduction: Traumatic brain injury (TBI) can impact patients' cognitive functioning and quality of life. This study assesses the effectiveness of cognitive interventions in TBI patients and examines factors influencing their success, aiming to enhance care and customize treatments for optimal rehabilitation outcomes. Methods: a systematic review of 31 scientific articles evaluating the effectiveness of cognitive rehabilitation in patients with traumatic brain injuries was conducted, following the PRISMA workflow. The studies covered the period from 2017 to 2021, and specific terms were used to search the PubMed and Scopus databases. Results: the research on cognitive interventions in patients with traumatic brain injuries has involved various countries, with notable contributions from the United States, Norway, the United Kingdom, and Canada. Cognitive training has proven to be effective, showing significant improvements in symptoms and quality of life. Other therapies, such as transcranial direct stimulation and vocational rehabilitation, have also been investigated. Conclusions: cognitive training has proven to be an effective technique in managing traumatic brain injuries, demonstrating significant improvements in composite cognitive measures and patients' quality of life. Some therapies, such as hyperbaric oxygen therapy, have shown promising results in treating symptoms such as post-traumatic stress, depression, and anxiety in patients with traumatic brain injuries.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".