Efficacy and safety of electroconvulsive therapy in the first trimester of pregnancy: a case of severe manic catatonia
Bibliographic record
Abstract
OBJECTIVES: Electroconvulsive therapy (ECT) is an appropriate, albeit often neglected, option for managing severe or life-threatening psychiatric symptoms during pregnancy. We report on the rapid effectiveness and safety of ECT during the first trimester of pregnancy in a 28-year-old woman with severe catatonia. METHODS: Catatonic symptoms were assessed using the Catatonia Rating Scale (CRS). The patient was treated with unilateral ECT using left anterior right temporal (LART) placement. Seizure quality and duration were monitored by a two-lead electroencephalograph (EEG) and by one-lead electromyography (EMG). During each ECT session, the fetal heart rate was monitored with electrocardiogram (ECG). RESULTS: After the second ECT treatment (day 13 of hospitalization), we observed remission of the catatonic symptoms, as shown by the drop in the CRS score from 22 to 0. No cognitive abnormalities were reported and no gynecological complications were detected (e.g. vaginal bleeding, abdominal pain, or uterine contraction). The patient delivered at term a healthy male neonate who presented normal growth as well as normal psychomotor development. CONCLUSIONS: This case highlights the effectiveness of ECT in treating severe catatonic mania during the first 3 months of pregnancy. In addition, ECT proved to be a safe therapeutic option, since neither mother nor infant experienced any adverse event. We suggest that ECT might be considered as a valid and safe option in the therapeutic decision-making process when catatonic symptoms manifest during pregnancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".