Cardiac picture archiving and communication systems and telecardiology-technologies awaiting adoption
Bibliographic record
Abstract
Diagnostic and therapeutic procedures associated with cardiology are heavily supported by diagnostic imaging technology. The management of such images, including radiographs, echocardiography examinations and cardiac angiography studies, requires a suitable means of handling the data. A number of manufacturers are now offering picture archiving and communication systems (PACS) and telecardiology options. These could greatly improve the efficiency of data management for cardiac examinations, including linkage to radiology and hospital information systems and electronic patient records. A barrier to the implementation of cardiac PACS has been the relatively high capital cost. There have also been technical difficulties in implementing a suitable interface. Historical problems have included 'turf wars' between different specialist groups and a reluctance to shift from well established practice patterns. Early cooperative work between radiologists and cardiologists in the development of coronary arteriography has been replaced by contention between cardiologists, radiologists and vascular surgeons, often driven by economic considerations rather than the needs of the patient. At this stage, cardiac PACS and telecardiology have great potential for improving the coordinated care of cardiac patients in Australia.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".