Electroconvulsive Therapy Clinical Database
Bibliographic record
Abstract
Across health care disciplines research reflects the usefulness of integrating computer technology into administrative and clinical practices. Electroconvulsive therapy (ECT) researchers are often interested in examining 3 primary areas: patient characteristics, treatment characteristics, and treatment outcomes. Generating reports and conducting research analysis via the traditional patient chart review are a time-consuming and costly method. At Riverview Hospital, a tertiary care psychiatric hospital, the active use of a clinical database for patients receiving ECT allows for detailed treatment tracking and evaluation of pretreatment and posttreatment patient outcome measures. Initially, designed as part of a quality improvement process to readily access patient information and generate periodic reports, the ECT clinical database is now a central resource for ECT-specific patient, treatment, and outcome tracking. The relevance, design, content variables, and subsequent functions of the entry and storage of ECT-related administrative, treatment, outcome, and patient factors are clearly outlined and discussed. Strengths and limitations to the existing database are shared. Recommendations to other ECT services to implement this valuable documentation strategy are addressed. This approach can be an invaluable tool in providing the field of psychiatry with further contributions to ECT clinical outcomes.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.020 |
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".