Transient Insight Induction With Electroconvulsive Therapy in a Patient With Refractory Schizophrenia
Bibliographic record
Abstract
Anosognosia or lack of illness awareness is a clinical manifestation of both schizophrenia and right hemispheric lesions associated with stroke, neurodegeneration, or traumatic brain injury. It is thought to result from right hemispheric dysfunction or interhemispheric disequilibrium, which provides a neuroanatomical model for illness unawareness in schizophrenia. Lack of insight contributes to medication nonadherence and poor treatment outcomes and is often refractory to pharmacological and psychological interventions. We present the first report of transient illness awareness (<8 hours) after individual bilateral electroconvulsive therapy treatments in the case of a 39-year-old man with antipsychotic refractory schizophrenia. Electroencephalography demonstrated frontal slow wave activity with shifting frontotemporal predominance, which was concurrent with the patient's transient level of insight. A systematic review of the literature on electroconvulsive therapy-induced illness awareness in schizophrenia and psychotic disorders produced zero relevant results. Future research should focus on the prospective role of focal interventions, such as transcranial magnetic stimulation, in the development of a neurophysiological model for anosognosia reversal in schizophrenia that may, in turn, contribute to novel therapeutic developments targeting lack of illness awareness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".