Pertinence of Titration and Age-Based Dosing Methods for Electroconvulsive Therapy
Bibliographic record
Abstract
BACKGROUND: Although the dosage of electroconvulsive therapy (ECT) stimulus has a major impact on the efficacy and safety of this treatment, the method used to determine an optimal dosage remains a matter of debate. OBJECTIVE: We investigated factors influencing the seizure threshold (ST) in a large-sample study and compared age-based and titration dosing methods in terms of charge. METHODS: A retrospective study examined data from 503 patients across France and Canada. The patients underwent right unilateral (RUL) or bitemporal (BT) ECT during a titration session before undergoing ECT. Seizure threshold and charge differences between age-based and titration-predicted methods were derived for each RUL and BT patient and compared according to sex, age, and anesthetic agents. RESULTS: Based on our results, ST is a function of electrode placement, sex, age, and anesthetic agents. Titration and age-based methods led to completely different patterns of charges for the same electrode placement, especially in elderly and in women in the RUL group. Regression models showed that differences between the age-based and titration methods varied with respect to age, sex, and anesthetic agent. Specifically, significant effects of sex and age were observed for RUL ECT and of sex and anesthetics for BT ECT. CONCLUSIONS: This study revealed that several factors significantly influence the prediction of ECT dose, depending on individuals and treatment modalities. Caution should be exercised when using nonindividualized methods to calculate ST.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".