The Assessment of Basic Features of Electroencephalography in Metabolic Encephalopathies
Bibliographic record
Abstract
Background: The comparison of the electroencephalography (EEG) data with the patients’ primary diagnosis and the relationship with the prognosis was assessed with this study in the cases that are being followed up with the diagnosis of metabolic encephalopathy (ME). Methods: A total of 306 patients who were being followed up due to ME between January 2009 and September 2011 were included in the study. The etiologic causes in the cases were detected as hyponatremia in 26.2%, hypoxic ischemic encephalopathy in 23.8%, renal failure in 14.4%, hepatic failure in 11.7%, diabetes mellitus in 8.2%, endocrinopathies except for diabetes mellitus in 8.8%, and hypernatremia in the remaining 6.9%. EEG examinations were performed with two different methods. Firstly, 269 of 367 EEGs were analyzed for baseline activity, divided in six stages. Results: Another assessment in EEG examination considering abnormal patterns was performed and 281 of 367 EEGs were taken into this assessment; reduction in the alpha, asynchronous slow waves, focal slow activities, triphasic waves, burst-suppression pattern, and generalized or focal spike-sharp activities were observed. There were no differences between the EEG groups statistically by age and sex. There were no statistical associations between diagnoses and the change of consciousness ( P = 0.187). There was no significant correlation between EEG findings and diagnostic groups ( P = 0.126) ; however , it was statistically shown that as the impaired con s ciousness increased, the EEG stages moved forward to worse stages (P < 0.001). Conclusion: We think that EEG examination does n o t contribute to the diagnosis of the etiology of the disease ; however , it may be useful in follow-ups and prognosis in ME. J Neurol Res. 2014;4(4):101-109 doi: http://dx.doi.org/10.14740/jnr285w
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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 | 0.002 |
| 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".