Bibliographic record
Abstract
Background Quality assurance (QA) of echocardiographic studies is vital to ensure that clinicians can act on findings of high quality to deliver excellent patient care. To date, there is a paucity of published guidance on how to perform this QA. The British Society of Echocardiography (BSE) has previously produced an Echocardiography Quality Framework (EQF) to assist departments with their QA processes. This article expands on the EQF with a structured yet versatile approach on how to analyse echocardiographic departments to ensure high-quality standards are met. In addition, a process is detailed for departments that are seeking to demonstrate to external bodies adherence to a robust QA process. Methods The EQF consists of four domains. These include assessment of Echo Quality (including study acquisition and report generation); Reproducibility & Consistency (including analysis of individual variability when compared to the group and focused clinical audit), Education & Training (for all providers and service users) and Customer & Staff Satisfaction (of both service users and patients/their carers). Examples of what could be done in each of these areas are presented. Furthermore, evidence of participation in each domain is categorised against a red, amber or green rating: with an amber or green rating signifying that a quantifiable level of engagement in that aspect of QA has been achieved. Conclusion The proposed EQF is a powerful tool that focuses the limited time available for departmental QA on areas of practice where a change in patient experience or outcome is most likely to occur.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".