To Study Risk of Seizures After Mild Traumatic Brain Injury in General Population (I13.009)
Bibliographic record
Abstract
Objective: To study risk of seizures after mild traumatic brain Injury in general population. Method: Perspective study of patients presented in TBI clinic for 2 yrs. On initial visit after neurological evaluation and detail questioning about the TBI and seizures episodes, Montreal cognitive assessment was administered to all patients. Routine EEG as a standard protocol was followed after neurological evaluation by a neurologist. Results: 134 patients presented to the TBI clinic in 2 years, after clinical interview 64 patients (47.7[percnt]) experienced transient loss of consciousness. With strict selection criteria for seizure episodes nine patients (6.7[percnt]) had one episode of overt seizures. In Loss of consciousness group 43.7[percnt] had abnormal EEG, and 14.06[percnt] in LOC group had reported seizure. A general linear model multifactor analysis of variance (ANOVA) showed loss of consciousness (p = 0.043) as the only factor directly relating to the demonstration of abnormal electrical discharges on EEG Conclusion: Almost 1.6 million individuals experience a mTBI, and are evaluated and released from an emergency department each year. Mild TBI comprises 70[percnt]-80[percnt] of all head injuries. We do not have any standard protocol for recommendation and follow up after mTBI patients are discharged from emergent care. In our study showed direct correlation of the loss of consciousness and the abnormal EEG as well as seizure episodes in mTBI patients.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".