‘How to do’: digital-interactive-interpretation course for stress echocardiography
Bibliographic record
Abstract
Why?: To improve stress echocardiography interpretation standards, we introduced a structured 5-day interpretation course in 2017. In order to deliver the course during the world-wide pandemic, we transformed the existing boardroom style, workstation-based, interactive course into a cloud-based digital entity maintaining the same features. How?: On completion of 6 lectures via live webinars, 15 participants performed, fully GDPR compliant, 80 recorded case analysis using a web-based reporting system over the course of 5 days. After self-reporting and generating preliminary reports the joint case review with the faculty, resulted in finalization of the reports and provided individual feedback for the participants. By the 5th day, participants had collected 80 reports for their e-logbook and were ready to sit the digital interpretation exam. Results: Eighty-eight percent of participants passed the e-exam and received a certificate of completion with 15 re-accreditation and 30 CPD points by the British Society of Echocardiography and Federation of the Royal Colleges of Physicians, UK, respectively. The feedback by the participants was praising the pre-course provision of lectures and digital aids, the conduct of the course by the faculty and the technical support with an average score of 4.7 for each, on a scale from 1 to 5. Conclusion: Our experience proved that interactive, multi-day; hands-on reporting course can be delivered using the digital platform. Online interpretation courses have great potential to improve the competency of imaging specialists. This digital teaching model could be suitable in other imaging-based training courses like cardiac CT and MRI.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".