Editorial Surface Electrocardiogram Remains Alive in the XXI Century
Bibliographic record
Abstract
All around the globe, the surface electrocardiogram (ECG) remains one of the most frequently used diagnostic tools in clinical practice. It provides useful information to guide initial treatment (e.g. in the emergency), it contributes to the diagnosis of acute and chronic diseases, and it also allows establishment of the prognosis and response to treatment in a large and varied amount of clinical conditions [1]. The ECG is not only useful for the cardiologist, but also for the internist, the family medicine practitioner, the neurologist and the emer-gency medicine doctor; naming just a few of the health care providers that are daily associated with the responsibility of reading and inter-preting ECGs. This is why, when Bentham Science Publishers offered me (AB) the opportunity to coordinate a special issue on electrocardio-graphy, I did not hesitate to accept the challenge. The first task was to create an editorial team of experts with experience in publishing on electrocardiography. In this regards I was able to enlist a fantastic group of collaborators, full of ideas and ready to work hard. From Argentina, Prof. Dr. Pablo Chiale from the “Buenos Aires School of Electrocardiography ” accepted the challenge immediately. Pablo has made major contributions to the specialty of electrocardiology over a wide variety of topics including the mechanisms and pharma-cologic treatment of arrhythmias, arrhythmias and auto-immune conditions, cardiac memory and physiopathology of the Brugada syndrome [2, 3]. Pablo has published extensively, and the topics mentioned represent a mere fragment of his expansive portfolio. As part of the team I wanted a “true educator”. To this end I was delighted that Prof. Dr. Martin Green from Canada kindly agreed to join
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".