Time to Benefit of Surgery vs Targeted Medical Therapy for Patients With Primary Aldosteronism: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Primary aldosteronism (PA) is one of the most common causes of secondary hypertension, but the comparative outcomes of targeted treatment remain unclear. OBJECTIVE: To compare the clinical outcomes in patients treated for primary aldosteronism over time. METHODS: Medline and EMBASE were searched. Original studies reporting the incidence of mortality, major adverse cardiovascular outcomes (MACE), progression to chronic kidney disease, or diabetes following adrenalectomy vs medical therapy were selected. Two reviewers independently abstracted data and assessed study quality. Standard meta-analyses were conducted using random-effects models to estimate relative differences. Time to benefit meta-analyses were conducted by fitting Weibull survival curves to estimate absolute risk differences and pooled using random-effects models. RESULTS: 15 541 patients (16 studies) with PA were included. Surgery was consistently associated with an overall lower risk of death (hazard ratio [HR] 0.34, 95% CI 0.22-0.54) and MACE (HR 0.55, 95% CI 0.36-0.84) compared with medical therapy. Surgery was associated with a significantly lower risk of hospitalization for heart failure (HR 0.48 95% CI 0.34-0.70) and progression to chronic kidney disease (HR 0.62 95% CI 0.39-0.98), and nonsignificant reductions in myocardial infarction and stroke. In absolute terms, 200 patients would need to be treated with surgery instead of medical therapy to prevent 1 death after 12.3 (95% CI 3.1-48.7) months. CONCLUSION: Surgery is associated with lower all-cause mortality and MACE than medical therapy for PA. For most patients, the long-term surgical benefits outweigh the short-term perioperative risks.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.063 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".