Combined therapy of positive interventions and cognitive training for reducing neurobehavioral symptoms of traumatic brain injury: A clinical case
Bibliographic record
Abstract
Introduction There is a need to study therapies that may contribute to the successful rehabilitation of veterans with traumatic brain injury (TBI) and increase their effective interaction with the stressful environment, reduce the severity of symptoms. Combined short-term therapies may have potential. Objectives To analyze the clinical case of combined psychological treatment of TBI in a Ukrainian combat veteran with reduced resilience Methods The clinical case of Ukrainian combat veteran with TBI is presented. Montreal Cognitive Assessment (MoCA) was used to assess cognitive domains. Neurobehavioral symptom inventory (NSI) was used to assess neurobehavioral symptoms of TBI. CD-RISC was used to assess resilience. In addition to pharmacotherapy, the patient agreed to undergo a combined program of psychological therapy of 3 short-term positive intervention sessions and 3 cognitive training sessions. Results MoCA result prior to treatment was 24 p., NSI – 38 p., CD-RISC – 44 p. (lower than in population). After the combined therapy, the results of the assessment with MoCA were 26 points, NSI was 17 points, CD-RISC – 47 points. Subjectively, the patient noted an improvement in emotional state, better resilience, and a significant reduction in the intensity of cognitive symptoms. Conclusions Combining positive interventions with cognitive training can have the potential to significantly improve the neurobehavioral and cognitive functioning of war veterans with traumatic brain injury, and also possibly increase resilience. Further research in this direction will be conducted to obtain more reliable results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".