Implantable Cardioverter-Defibrillators and Survival – The Fine Line Between Efficacy Concerns and Ageism
Bibliographic record
Abstract
This article refers to 'One-year mortality after implantable cardioverter-defibrillator placement within the Veterans Affairs Health System' by M. Fudim et al., published in this issue on pages 859-867.In this issue of the Journal, Fudim et al. 1 examine all-cause mortality over a median follow-up of 2.1 years in 17 901 patients with heart failure and reduced ejection fraction receiving an implantable cardioverter-defibrillator (ICD) in the Veterans Affairs Health System.As expected in the Veterans Affairs Health System, the population was predominantly male (99%) with ischaemic cardiomyopathy (81% coronary artery disease), frequent cardiovascular risk factors, but only modest comorbidity burden [mean Charlson comorbidity index (CCI) 3.0].The analysis treads a familiar path of questioning the value of ICDs with advancing age, focusing on 1-year mortality which averaged 13% and was unchanged from 2007 to 2014.Independent predictors of death included age, coronary artery disease and risk factors, and non-cardiac conditions.The relationship between age and 1-year mortality was striking, rising from a few percentage age 40, to 30% in nonagenarians.Expressed by age quartile, 1-year mortality rose from 7% to 32% from the lowest to the highest quartile.Though tempting to largely attribute the relationship between age and survival to comorbidities, several observations temper this argument.First, only a minor increase in CCI occurred from youngest to oldest quartiles (2.75 to 3.07, respectively).Second, an extreme increase in CCI only correlated with a modest increase in 1-year mortality from around 10% to 20%.Third, comorbidities actually declined from 2007 to 2014 (CCI 3.1 to 2.8, P < 0.001), while overall mortality remained stable.Finally, age remained a powerful independent predictor of survival after adjustment for individual comorbidities.The opinions expressed in this article are not necessarily those of the Editors of the
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".