The EKG in cardiac MRI: what the technologist needs to know
Bibliographic record
Abstract
In routine cardiac MRI (CMRI) practice, radiological technologists are expected to optimize and utilize EKGs in gating and physiological monitoring. Many cardiac sequences are critically dependent on heart rate and rhythm and EKG gating problems lead to poor or non-diagnostic images. The technologist's syllabus does not routinely include formal EKG recognition or CMRI trouble-shooting techniques. In this educational exhibit, we describe common EKG rhythms and discuss imaging parameter options for overcoming magneto-hemodynamic effects, dealing with high or low heart rates and abnormal rhythms. An image sequence/EKG rhythm quick algorithm is included for reference and will be the basis of the exhibit. Basic EKG rhythm recognition and imaging optimization tips are the building blocks of good cardiac MRI - something every good CMRI technologist should know but are rarely formally taught.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.017 |
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".