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Record W4413970123 · doi:10.1093/ajh/hpaf156

Rethinking Primary Aldosteronism: The 2025 John H. Laragh Research Award

2025· article· en· W4413970123 on OpenAlexafffund
Gregory L. Hundemer, Alexander A. C. Leung, Gregory Kline

Bibliographic record

VenueAmerican Journal of Hypertension · 2025
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of CalgaryUniversity of Ottawa
FundersCanadian Institutes of Health ResearchKidney Foundation of Canada
KeywordsMedicineThe RenaissancePrimary aldosteronismIntensive care medicineDiseaseSubclinical infectionAldosteronePathologyInternal medicine

Abstract

fetched live from OpenAlex

Dr John Laragh, a pioneer in the field of hypertension, held a fundamental belief in the need to challenge existing dogmas in medicine to enhance our scientific understanding of disease and advance patient care. Perhaps in no area of hypertension does this ring truer with than primary aldosteronism (PA). Following its initial description in the mid-1950s with an initial surge in diagnoses soon thereafter, PA was ultimately relegated to be considered a "zebra" of hypertension felt to be responsible for only a small percentage of cases. In turn, diagnostic and treatment pathways went largely unchanged for decades. However, recent years have witnessed a renaissance in PA sparked by a number of scientific breakthroughs that have re-fashioned many long-held paradigms. Such breakthroughs include the recognition that classically defined PA is highly prevalent and that an even broader spectrum of milder forms of dysregulated aldosterone production (subclinical PA) goes completely unrecognized in modern-day clinical practice. Further, a number of pitfalls have recently been exposed across all steps of traditional PA diagnostic pathways, explaining many prior missed cases and missed opportunities. Acknowledging these pitfalls may allow for streamlined, practical, and evidence-based algorithms in the future to enhance disease detection. Finally, treatment approaches are now evolving with new strategies to better guide existing medical therapies along with emerging novel therapeutic options which may transform PA management in the future. This review discusses these recent advances in our understanding of PA and sheds light on what the future of PA management may entail.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.354
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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