The Use of Electroconvulsive Therapy in Special Patient Populations
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite its well-established efficacy and its increasing use, electroconvulsive therapy (ECT) remains a controversial treatment. Lack of clarity in the issues related to its use in special patient populations (for example, in children, in adolescents, in pregnant women, in the elderly, and in the medically ill) often contributes to the debate about the use of ECT. METHOD: The literature on ECT use in special patient populations is reviewed, together with the commonly associated high-risk medical conditions in clinical practice. Specific reference is made in each case to the safety, tolerability, and efficacy of the procedure. RESULTS: Much of the literature surveyed consists of case studies, although a few controlled trials are available. In general, ECT use in special populations is relatively safe and extremely effective. In small case series, ECT use in children and adolescents is effective but requires further systematic study. In pregnant women, ECT is very effective, and with proper medical care, it is relatively safe in all trimesters of pregnancy, as well as in the postpartum period. The frail elderly are particularly good candidates for ECT because they are often unresponsive to or intolerant of psychotropic medication. Medical conditions that should receive particular attention during a course of ECT are disorders of the central nervous system (CNS), cardiovascular, and respiratory system. With modern anesthesia techniques and careful medical management of each high-risk patient, most can successfully complete a course of ECT. The process of obtaining informed consent also requires special consideration in this group of patients because their capacity to consent to treatment may be compromised. CONCLUSIONS: With careful attention to each patient's medical and anesthesia needs, ECT is an effective and relatively safe procedure in high-risk special patient populations.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it