Comparison of Electroconvulsive Therapy Practice Between London and Bengaluru
Bibliographic record
Abstract
OBJECTIVE: To compare electroconvulsive therapy (ECT) practice between London in the United Kingdom and Bengaluru in India. METHODS: A retrospective case note study was conducted to compare patterns of referrals for ECT in university teaching hospitals in London (n = 46) and Bengaluru (n = 345) during a 1-year period. Further comparison of ECT practice was made for a consecutive series of depressed patients between London (n = 104) and Bengaluru (n = 125). RESULTS: The rates of ECT referral were 0.9% of total annual admissions at the London site and 8.2% at the Bengaluru site. At the Bengaluru site, a higher proportion of patients were referred for ECT with a diagnosis of schizophrenia (P < 0.0001). Compared to the Bengaluru sample, depressed patients treated with ECT in London (n = 104) were older with more treatment resistance (P < 0.0001), had longer inpatient stays, and were less responsive to ECT. CONCLUSIONS: The practice of ECT differed substantially between the London and Bengaluru sites. The relatively limited use of ECT in London reflects local treatment guidelines and may reflect the stigma associated with ECT. Electroconvulsive therapy is more widely used in Bengaluru with good outcomes. Further cross-cultural research is required to study the reasons for such contrasting practices and what constitutes the optimal practice of ECT for health systems in different countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".