Analysis of influencing factors of cognitive impairment after moderate traumatic brain injury
Bibliographic record
Abstract
Objective To analyze the influencing factors of cognitive impairment after moderate traumatic brain injury and to develop prognostic models for cognitive impairment after moderate traumatic brain injury. Methods A prospective study was performed on 104 patients with moderate traumatic brain injury at Department of Neurosurgery, the Third Xiangya Hospital, Central South University from November 2016 to February 2018. The cognitive function at 3 months after injury was assessed using the Montreal cognitive assessment (MoCA) score. The impact of various lesions on cognitive function was analyzed using univariate and multivariate logistic regression. Prognostic models was established based on logistic regression analysis results. The validation sampling was used to compute the accuracy, sensitivity and specificity of the prognostic models. Results Logistic regression analysis revealed that age (OR=1.118, 95%CI: 1.000-1.250, P=0.049), education (OR=0.202, 95%CI: 0.041-0.988, P=0.045), Glasgow coma scale (GCS) score (OR=2.582, 95%CI: 1.242-5.369, P=0.011) and injury cause (OR=0.429, 95%CI: 0.201-0.915, P=0.029) were independent risk factors of cognitive impairment after moderate traumatic brain injury. The prognostic model based on the risk factors of admission had favorable performance (P>0.05 for partial chi square test, 0.902 for area under curve). The accuracy of the prognostic model was 88.6%. The sensitivity and specificity were 55.6% and 100.0% respectively. Conclusions Age, education, GCS score and injury cause are influencing factors of cognitive impairment after moderate traumatic brain injury, based on which the established model could be used to timely and accurately predict the prognosis of cognitive impairment after moderate traumatic brain injury. Key words: Craniocerebral trauma; Cognitive disorder; Logistic models; Forecasting models
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".