Performance of the Aldosterone to Renin Ratio as a Screening Test for Primary Aldosteronism
Bibliographic record
Abstract
CONTEXT: The aldosterone to renin ratio (ARR) is the guideline-recommended screening test for primary aldosteronism. However, there are limited data in regard to the diagnostic performance of the ARR. OBJECTIVE: To evaluate the sensitivity and specificity of the ARR as a screening test for primary aldosteronism. METHODS: We searched the MEDLINE, Embase, and Cochrane databases until February 2020. Observational studies assessing ARR diagnostic performance as a screening test for primary aldosteronism were selected. To limit verification bias, only studies where dynamic confirmatory testing was implemented as a reference standard regardless of the ARR result were included. Study-level data were extracted and risk of bias and applicability were assessed using the QUADAS-2 tool. RESULTS: Ten studies, involving a total of 4110 participants, were included. Potential risk of bias related to patient selection was common and present in half of the included studies. The population base, ARR positivity threshold, laboratory assay, and reference standard for confirmatory testing varied substantially between studies. The reported ARR sensitivity and specificity varied widely with sensitivity ranging from 10% to 100% and specificity ranging from 70% to 100%. Notably, 3 of the 10 studies reported an ARR sensitivity of <50%, suggesting a limited ability of the ARR to adequately identify patients with primary aldosteronism. CONCLUSIONS: ARR performance varied widely based on patient population and diagnostic criteria, especially with respect to sensitivity. Therefore, no single ARR threshold for interpretation could be recommended. Limitations in accuracy and reliability of the ARR must be recognized in order to appropriately inform clinical decision-making.
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 | 0.044 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".