Audit of Electroconvulsive Therapy Service Provision in Lincolnshire Partnership Foundation Trust: Current Standards and Adherence to National Guidance
Bibliographic record
Abstract
Aims To improve the quality of care received by service users of Electroconvulsive Therapy (ECT) treatment in Lincolnshire Partnership Foundation Trust (LPFT) by measuring the compliance of the local ECT clinic in Lincolnshire in accordance with National Institute of clinical excellence guidance and ECT accreditation services standards. Methods Pre-audit work up includes consultations with ECT clinic lead and stake holders to ensure ethical and governance standard are met. This audit is conducted with the permission of trust quality and safety team. Sample population is identified from ECT clinic registry, Lincoln. A total of 10 patients who received ECT treatment between January 2023 and August 2023 are included regardless whether the necessary information is available on the clinical system or not, to minimise selection bias. Retrospective data collection by using Rio electronic case records. Descriptive analysis of data using Microsoft Excel and evaluation of results is based on 3 key domains such as indication, consent process and monitoring. Results A total of 10 service users, comprising 30% males and 70% females, underwent treatment in both inpatient (80%) and outpatient (20%) settings, primarily for severe depressive illness. In 70% of cases, a pre-ECT assessment was documented to evaluate potential risks and benefits. The consent procedure was completed by a psychiatrist in 70% of instances. However, ongoing consent was not consistently reviewed at each ECT treatment. Baseline monitoring using the Clinical Global Impression and Comprehensive Psychopathological Rating Scale was conducted in 20% of cases, with no follow-up assessments performed after each treatment. The Montgomery–Åsberg Depression Rating Scale was employed at baseline for 40% of patients, yet there was no evidence of weekly monitoring. While the Montreal Cognitive Assessment was administered to all patients at baseline, it was not conducted after every four treatments. Post-ECT follow-up data revealed that less than a quarter of patients underwent clinician reviews. Validated rating scales were utilized in no more than a fifth of patients at both one week and two months after treatment. Conclusion The findings suggest the need for improved documentation of the entire consent process and in regularly assessing the ongoing validity of consent. Moreover, there is a need for stronger monitoring at baseline, during, and after ECT treatment. It is recommended to revise the local ECT record pathway by December 2023, with a follow-up re-audit scheduled for March 2024 to evaluate the effectiveness of the implemented changes.
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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 | 0.064 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".