Effect of Cognitive Behavioral Therapy on Stress Disorder, Cognitive Function, Motor Function, and Daily Living Ability of Patients with a Traumatic Brain Injury
Post-publication record
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Bibliographic record
Abstract
Purpose. The aim of the study is to observe the effects of cognitive behavioral therapy on stress disorder, cognitive function, motor function, and daily living ability of traumatic brain injury (TBI) patients. Methods. 84 patients with TBI admitted to our hospital from June 2019 to May 2021 were selected as the research subjects. They were divided into a control group (from June 2019 to May 2020) and an observation group (from June 2020 to May 2021), with 42 cases in each group. The control group received routine intervention; the observation group received cognitive behavioral therapy on the basis of the control group. Before and after intervention, the posttraumatic stress disorder (PTSD), cognitive function, motor function, and daily living ability of the two groups were observed. Results. After intervention, the PTSD-self-rating scale (PTSD-SS) scores of both groups were lower than those before intervention, and the PTSD-SS scores of the observation group were lower than those of the control group P < 0.05 . After intervention, the scores of the Montreal cognitive assessment (MoCA) scale, Fugl-Meyer assessment (FMA), and modified Barthel index (MBI) in both groups were higher than those before intervention, and the scores of MoCA, FMA, and MBI in the observation group were higher than those in the control group P < 0.05 . Conclusion. The application of cognitive behavioral therapy to TBI patients is beneficial to reduce the degree of PTSD and improve cognitive function, motor function, and daily living ability, which is worthy of clinical application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".