Should the implantable cardiac defibrillator be used for primary prevention of sudden death? A review of the issues relevant to hospital decision making.
Bibliographic record
Abstract
BACKGROUND: New evidence suggests that the implantable cardiac defibrillator (ICD) may be effective for primary prevention of sudden death. High instrumental cost and the potentially large number of candidates will significantly impact hospital budgets. OBJECTIVE: To review the information relevant to hospital policy decisions on the use of ICDs for primary prevention. METHODS: Modelling based on an evaluation of reported studies and Canadian costs, detailed in a comprehensive review available at . RESULTS: Health outcomes: Two high-quality primary prevention trials suggest a reduction in annual mortality of 2.9% with bounds of probability (PB) of 2.0% to 3.8%, in early years. To undertake 100 implants per year may result in an increasing annual saving of life-years of acceptable quality, stabilizing by 15 years at 110 (PB 72 to 154) each year. Economic impact: The annual costs would stabilize at 15 years at approximately 4.3 million dollars. Cost-effectiveness: The incremental cost-effectiveness from the point of view of the health care system would be approximately 47,000 dollars (PB 35,000 dollars to 70,000 dollars), discounted at 3%. Ethical and legal issues: The decision should not be individual but institutional, using a process that is transparent, consistent and fair. CONCLUSIONS: The ICD can prolong life, with acceptable quality. Restriction of its use would be solely on grounds of expense.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".