Complications of general anaesthesia in electroconvulsive therapy
Bibliographic record
Abstract
usually related to intravenous infusion of anaesthetics in general anaesthesia, epileptic seizures intrinsic to electroconvulsive therapy treatment as well as interactions with other medicinal products.Literature review.The most common causes of death during electroconvulsive therapy are cardiac arrhythmia and acute coronary syndrome.The most frequent complication related to electroconvulsive therapy treatment is craniofacial trauma, especially dental and tongue injuries.Additionally, it is possible to observe complications within the respiratory system (prolonged apnoea, aspiration pneumonia, bronchospasm), nervous system (subarachnoid haemorrhage, subdural hematoma), or cardiovascular system (takotsubo cardiomyopathy).Conclusions.The American Psychiatric Association (APA) does not point to absolute contraindications to electroconvulsive therapy; nevertheless, there are medical conditions that involve an increased risk of adverse events.However, when analysing the position of electroconvulsive therapy in the treatment of mental disorders, one should not only take into account the high effectiveness of the method, the transient nature of most side effects, and the relatively rare occurrence of serious and life-threatening somatic complications, but it should also be remembered that electroconvulsive procedures are often the treatment of choice and a rescue procedure saving the sick person's life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".