The importance of a comprehensive evaluation of survivors of cardiac arrest
Bibliographic record
Abstract
This editorial refers to ‘Characteristics and clinical assessment of unexplained sudden cardiac arrest in the real-world setting: focus on idiopathic ventricular fibrillation’†, by V. Waldmann et al., on page 1981. In the absence of structural heart disease, an apparently unexplained cardiac arrest includes an eclectic compilation of latent causes, including long QT syndrome, Brugada syndrome, early repolarization syndrome, and several structural causes. After comprehensive assessment, about half of patients with unexplained cardiac arrest will have a specific aetiology identified, with the remaining indeterminate cases labelled ‘idiopathic ventricular fibrillation’ (IVF).1 In this issue of the journal, Waldmann et al. report the medical evaluation and outcomes among a Parisian cohort of 717 cardiac arrest survivors, focusing on those patients who remained undiagnosed after diagnostic testing.2 Among patients labeled IVF (49 patients), only 16% received a comprehensive assessment, defined as cardiac magnetic resonance (CMR), ergonovine challenge, and pharmacological testing. Although most patients underwent CMR (82%), genetic testing was performed in less than one-fifth and exercise testing in less than one-tenth of individuals. Importantly, family screening occurred in less than a quarter of patients. The authors’ findings highlight the importance of a comprehensive systematic evaluation of cardiac arrest survivors, and gaps in the current delivery of guideline-directed unexplained cardiac arrest assessment.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".