Cognitive impairment and neuropsychiatry manifestation following mild and moderate traumatic brain injury at 3 month and 6 months
Bibliographic record
Abstract
Background and Objective \nCognitive impairment (CI) and neuropsychiatry manifestation (NM) are known \ncomplications among patients with traumatic brain injury (TBI). However, the clinical \ncorrelation of mild and moderate TBI with CI and NM have not been extensively studied \nin Malaysia. Our objectives were to determine i) the prevalence of CI and NM in mild \nand moderate TBI, ii) association between mild and moderate TBI with CI and NM and \niii) association between risk factors with CI and NM. \nMethods \nPatients (n=54) were divided into mild and moderate TBI. Both mild (n=26) and moderate \n(n=28) TBI were assessed at 3 months and 6 months post trauma for the same measures. \nDiagnosis of CI was made by using The Montreal Cognitive Assessment (MoCA) while \nNM screening was done using General Health Questionnaire-12 (GHQ-12) followed by \nMINI International Neuropsychiatry Interview (MINI) for diagnostic purpose. Univariate \nanalyses were done using chi square tests and multivariate analysis with multiple logistic \nregression test. \nResults \nWe found 5 patients (19.2%) with mild TBI had CI and 5 patients (19.2%) had NM at 3 \nmonths. Only 1 patient (3.8%) persistently has CI at 6 months while the rest recovered. \nAs for moderate TBI, 11 patients (39.3%) had CI and 7 patients (25%) had NM at 3 \nmonths but none had persistent CI or NM at 6 months. Age Umur (p<0.05, OR 0.678, CI \n0.463 – 0.995) and blood pressure were significant risks ((p< 0.05, OR 1.223, CI: 1.001 \n– 1.495) for CI and NM at 3 months. \nConclusion \nThis study highlighted the importance of screening for both CI and NM following mild \nand moderate TBI at 3 months and 6 months. These data are essential to help treating \nclinicians identifying potential risk factors among post head trauma patients. Therefore, \nearly recognition facilitates effective rehabilitation programs planning hence improve \nprognosis in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".