Decision-making in elderly patients with severe aortic stenosis: why are so many denied surgery?
Bibliographic record
Abstract
AIMS: To analyse decision-making in elderly patients with severe, symptomatic aortic stenosis (AS). METHODS AND RESULTS: In the Euro Heart Survey on valvular heart disease, 216 patients aged > or =75 had severe AS (valve area < or =0.6 cm(2)/m(2) body surface area or mean gradient > or =50 mmHg) and angina or New York Heart Association class III or IV. Patient characteristics were analysed according to the decision to operate or not. A decision not to operate was taken in 72 patients (33%). In multivariable analysis, left ventricular (LV) ejection fraction [OR = 2.27, 95% CI (1.32-3.97) for ejection fraction 30-50, OR = 5.15, 95% CI (1.73-15.35) for ejection fraction < or =30 vs. >50%, P = 0.003] and age [OR = 1.84, 95% CI (1.18-2.89) for 80-85 years, OR=3.38, 95% CI (1.38-8.27) for > or =85 vs. 75-80 years, P = 0.008] were significantly associated with the decision not to operate; however, the Charlson comorbidity index was not [OR = 1.72, 95% CI (0.83-3.50), P = 0.14 for index > or =2 vs. <2]. Neurological dysfunction was the only comorbidity significantly linked with the decision not to operate. CONCLUSION: Surgery was denied in 33% of elderly patients with severe, symptomatic AS. Older age and LV dysfunction were the most striking characteristics of patients who were denied surgery, whereas comorbidity played a less important role.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".