Characteristics and associated factors of early cognitive dysfunction following acute traumatic brain injury
Bibliographic record
Abstract
Objective To analyze the characteristics and associated factors of early cognitive dysfunction following acute traumatic brain injury (TBI) and provide the evidence for early diagnosis and treatment of cognitive dysfunction. Methods A prospective study was performed on 328 patients with mild to moderate TBI from Shanghai Pudong New Area People's Hospital since June 2012 to June 2014, using the Mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) to assess their cognitive function. Differential analysis of the mechanism of injury, age, gender, years of education, and CT manifestation type was performed in patients with and without cognitive dysfunction. Logistic regression analysis was further used to analyze the risk factors for cognitive dysfunction. Results In enrolled 328 patients, 56 patients (17.1%) were identified with cognitive dysfunction in MMSE score, while 207 patients (63.1%) in MoCA score. Cognitive dysfunction mainly manifested as visual-spatial and executive function, attention and calculation ability, language, abstract, and delayed memory. There were significant differences in years of education and CT manifestations (subarachnoid hemorrhage, contusion, intracranial hematoma, etc) between patients with and without cognitive dysfunction (P<0.01). Logistic regression analysis showed years of education, brain contusion, and intracranial hematoma were the major factor for cognitive dysfunction following TBI (P<0.01). Conclusion Cognitive dysfunction is largely affected by years of education, brain contusion and intracranial hematoma after TBI. Key words: Craniocerebral trauma; Cognition disorders; 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 | 0.001 |
| 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".