Primary Prevention of Fatal Ventricular Arrhythmias With Implantable Cardioverter- Defibrillator Therapy – An Analysis of Implications Based on MADIT II Criteria
Bibliographic record
Abstract
AIMS: The primary aim of this retrospective study was to determine the proportion of patients with myocardial infarction (MI) who fulfil the criteria of the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II and the implications of MADIT II criteria in practice. METHODS: We performed a retrospective analysis of three hundred and ninety four admissions to the Coronary Care Unit (CCU) of the Royal Infirmary of Edinburgh. We selected those with myocardial infarction (MI) and attempted to retrieve electronic copies of their echocardiogram reports. When available, these were used to assess requirement for primary-prevention Implantable Cardioverter Defibrillator (ICD) therapy based on reported left ventricular function. RESULTS: One hundred and ninety patients were admitted to the CCU with a diagnosis of MI. Of these, 100 patients (51.5%) had an echocardiogram. Requirement for ICD therapy was unlikely in 87 (87%), probable in 6 (6%) and necessary in 7 (7%). Since a significant number of patients in the probable category were also likely to meet MADIT II criteria, we concluded that the proportion of patients requiring primary-prevention ICD therapy would be no less than 7% and more likely to be 13%. CONCLUSION: In the context of a busy teaching hospital, a figure of 13% for the requirement of ICD therapy in MI patients represents annual implantation activity of at least 100 per million. This is likely to have very significant resource implications.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".