The effect of cognitive rehabilitation training based on games on cognition of the traumatic brain injury patients
Bibliographic record
Abstract
Objective To observe the effect of cognitive rehabilitation training based on games on cognition of the traumatic brain injury (TBI) patients.Methods 60 cases of patients according with the inclusion and exclusion criteria were divided into the control group and the training group with 30 cases in each group.The Montreal cognitive assessment (MoCA) test scores of all patients were lower than 26 points.The training group accepted the cognitive rehabilitation training based on games such as exactly the same game,drum game and picture memory game,while the control group did not accept the training.After one month,all patients accepted the MoCA test again.Then we analyzed the differences of the cognition between the two groups.Results After 30 days of training,all items of the cognitive function increased except the sub-item of abstraction.While in the control group,only the scores of attention,delayed recall,orientation and the total score showed alleviation.And all the scores of the training group were higher than those of the control group except the sub-item of abstraction.Conclusions Cognitive rehabilitation training based on games can effectively improve the cognitive function of TBI patients. Key words: Traumatic brain injury; Cognitive function; Montreal cognitive assessment; Cognitive rehabilitation training
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".