Nonlinear Indices with Applications to Schizophrenia and Bipolar Disorder.
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Bibliographic record
Abstract
In this article we study the application of nonlinear indices (sometimes called complexity indices) to univariate time series data arising from studies of schizophrenia and bipolar disorder. Specifically, we consider time series arising from EEG studies, ECG studies, and self-report mood data. As part of our analysis, we empirically examine the claim in the literature that complexity tends to be higher in the EEG of schizophrenia patients than controls and that this tendency is dampened or even inverted by medication, increasing age, and reduced symptomatology. Our conclusion is that this claim is only partially supported and propose that symptomatology, specifically the presence or absence of schizophrenia 'deficit syndrome,' may be the most important factor. Results are more consistent in ECG studies in which reduced heart rate complexity is observed in both schizophrenia and bipolar disorder. The applications of nonlinear indices to the effects of antipsychotic medication and the discrimination of mood states are also examined. It is concluded that the monitoring of nonlinear indices may be useful in predicting response to medication and predicting onset of specific mood states.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it