Description of anesthetic drugs used in hospital del Mar and their impacts on convulsion duration and blood pressure in electroconvulsive therapy
Bibliographic record
Abstract
Introduction The electroconvulsive therapy (ECT) is an effective treatment used for several psychiatric disorders. However, there are multiple enigmas about the mechanisms of action and factors that improve its results. Some frequent questions are if the anesthetic drug makes a difference in the time of convulsion and blood pressure. Aims Our principal aim is to describe the utilization of anesthetic drugs among the patients that are being treated with ECT in hospital del Mar. We also want to know the differences in the time of convulsion and systolic arterial pressure for every anesthetic drug (propofol, thiopental and etomidate). Material and methods We have used the database of ECT in hospital del Mar. It contains information like age, principal diagnosis, medical background and pharmacological treatment at the moment of starting ECTs; it also contains information of each individual ECT session as basal, 2 and 5 minutes arterial pressure; the anesthetic drug used, and convulsion duration. We made an analysis of general conditions of the population, the differences of convulsion time and arterial pressure between the three anesthetic drugs. Results Propofol was used in 1140 sessions, thiopental in 61 sessions and etomidate in 54 sessions. The differences in the means of convulsion times between propofol and etomidate are statistically significant (“P” value < 0.05). Etomidate or thiopental increases the difference of arterial pressure more than propofol. Conclusions Further research about the factors that improve convulsion duration and minimize adverse effects on blood pressure is needed. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| 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) | 0.005 | 0.001 |
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".