Treating electrical instability in sudden cardiac death survivors – are we looking at the right side of the coin?
Bibliographic record
Abstract
This editorial refers to “Response of programmed electrical stimulation and clinical outcome in cardiac arrest survivors receiving randomized assignment to implantable defibrillator or antiarrhythmic drug therapy”1 by Cappato et al. on page 642. The chances of surviving sudden cardiac death (SCD) are low (5–60%)1 and the recurrence rate is high (40% in the following 2 years). Secondary prevention in SCD-survivors is mandatory. SCD remains the “challenge to contemporary cardiology” as B. Lown wrote in the late 1970s. In SCD survivors the tool of choice is the implantable cardioverter-defibrillator (ICD), a crude but effective device that increases survival \(>\) 90%.1 It prolongs survival, but also reduces the quality of life. However, in view of the 40% recurrence rate of lethal, arrhythmic events, the ICD is implanted in 60/100 patients unnecessarily! Despite the efficacy of ICD therapy, its high costs are a considerable burden for the community, making a more precise risk stratification necessary to identify patients at the highest risk of recurrence of sudden arrhythmic death among the survivors who would most benefit from ICD.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".