Effect of quantitative consciousness index on seizure parameters during electroconvulsive therapy in patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) is both an effective treatment for patients with major depressive disorder (MDD) and a noxious stimulus. Although some studies have explored the effect of sedation depth on seizure parameters in ECT, there is little research on the noxious stimulation response to ECT. In this study, we used two electroencephalography (EEG)-derived indices, the quantitative consciousness (qCON) index and quantitative nociceptive (qNOX) index, to monitor sedation, hypnosis, and noxious stimulation response in patients with MDD undergoing acute ECT. AIM: To evaluate the effect of anesthesia depth based on the qCON and qNOX indices on seizure parameters. METHODS: = 24) underwent acute bilateral temporal ECT under propofol anesthesia. Before ECT, the patients were randomly divided into three groups according to qCON scores (qCON60-70, qCON50-60, and qCON40-50). Continuous qCON monitoring was performed 3 minutes before and during ECT, and the qCON, qNOX, vital signs, EEG seizure parameters, and complications during the recovery period were recorded. The 24-item Hamilton Rating Scale for Depression, Zung's Self-rating Depression Scale, and Montreal Cognitive Assessment scores were evaluated before the first ECT session, after the fourth ECT session, and after the full course of ECT. RESULTS: > 0.05). CONCLUSION: Electrical stimulation at a qCON index of 60-70 resulted in better EEG seizure parameters without increasing complications in patients with MDD undergoing bilateral temporal ECT under propofol anesthesia.
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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 | 0.000 | 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 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".