The preliminary research on the characteristics of cognitive function and its related factors in patients with severe traumatic brain injury
Bibliographic record
Abstract
Objective To investigate the characteristics of cognitive function in patients who suffered from severe traumatic brain injury (sTBI) at 3 months after treatment,and study the relationship between the characteristics and its related factors.Methods Montreal cognitive assessment (MoCA),Loewenstein occupational therapy cognitive assessment (LOTCA),mini-mental state examination (MMSE) scales were used to evaluate cognitive function in 30 normal individuals (control group) and in 21 patients who suffered from unilateral decompression craniectomy and hematoma and (or) contusion foci removed for decompression (sTBI group).The relationship between the results and its related factors was analyzed by multivariate statistical methods.Results All the scores of cognitive function assessment at 3 months aftercuring in sTBI group were significantly lower than those in control group,the ability of computing,repetition and fluency of language,logical thinking,attention,delayed memories,abstraction in patients with left hemisphere injury were significantly lower,while the ability of visuospatial and watch test,spatial perception,organization in patients with fight hemisphere injury were significantly lower.Multivariate statistics showed that age(r ＝ -0.722,P＜ 0.01 ),injury parts(r ＝ 0.607,P＜ 0.01 ),and the level of education (r ＝ 0.733,P ＜ 0.01 ) had significant impact on the overall cognitive function.Conclusion Cognitive dysfunction after sTBI is closely related with age,injury parts and the level of education. Key words: Craniocerebral trauma; Cognition discorders; Influence factors
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".