Neuropathologic Examination After 91 ECT Treatments in a 92-Year-Old Woman With Late-Onset Depression
Bibliographic record
Abstract
Whereas pathological seizure states, such as temporal lobe epilepsy, are commonly associated with cell loss and glial scarring in the hippocampus, seizures induced via electroconvulsive therapy (ECT) have not been associated with histological evidence of neuronal damage. We present a case report including the late-life medical history and postmortem histology of an elderly woman with major depression who received 91 sessions of ECT during the last 22 years of her life. Given the large number of ECT sessions, and her advanced age, this case provides a strong test of whether ECT causes detectable evidence of neuronal damage. We examined the gross morphology of the hippocampus, hippocampal cytoarchitecture, and measures of neuropathology. We found no pathological changes that could be attributed to ECT. Only expected, age-related features were present. Corpora amylacea and rare neurofibrillary tangles were evident, but we failed to detect any obvious evidence of cell loss or gliosis. Cognition in this patient was intact as indicated by a perfect score on a Mini-Mental Status Examination administered 6 days before death at the age of 92. This case adds to the considerable evidence for the safety of ECT.
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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 | 0.000 |
| 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) | 0.001 | 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".