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Record W4403103488 · doi:10.1111/cen.15139

Adrenal Vein Sampling for Primary Aldosteronism: Recommendations From the Australian and New Zealand Working Group

2024· review· en· W4403103488 on OpenAlexfundno aff
Jun Yang, Damon A. Bell, Richard Carroll, Cherie Chiang, Diane Cowley, Emma E. Croker, James C.G. Doery, Marianne S. Elston, Paul Glendenning, Julie Hetherington, Andrea R. Horvath, Shanshan Lu‐Shirzad, Elisabeth Ng, Amanda Mather, Nimalie Perera, Muddassir Rashid, Nirupa Sachithanandan, Jimmy Shen, Michael Stowasser, Michael J. Swarbrick, Hong Tan, Moe Thuzar, Simon W. Young, Winston Chong

Bibliographic record

VenueClinical Endocrinology · 2024
Typereview
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsnot available
FundersMonash UniversityEndocrine Society of AustraliaPartenariat Canadien Contre Le CancerRoyal College of Pathologists of Australasia
KeywordsPrimary aldosteronismMedicinePrimary careSampling (signal processing)Multidisciplinary approachPatient careTertiary careHealth careIntensive care medicineSurgeryFamily medicineInternal medicineAldosteroneNursingComputer science

Abstract

fetched live from OpenAlex

Adrenal vein sampling (AVS) is the current recommended procedure for identifying unilateral subtypes of primary aldosteronism (PA), which are amenable to surgery with the potential for cure. AVS is a technically challenging procedure usually undertaken by interventional radiologists at tertiary centres. However, there are numerous variations in AVS protocols relating to patient preparation, sampling techniques and interpretation which may impact the success of AVS and patient care. To reduce practice variations, improve the success rates of AVS and optimise patient outcomes, we established an Australian and New Zealand AVS Working Group and developed evidence-based expert consensus recommendations for the preparation, performance and interpretation of AVS. These recommendations can be used by all healthcare professionals in a multidisciplinary team who look after the diagnosis and management of PA.

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)0.000
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.372
GPT teacher head0.474
Teacher spread0.102 · 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
GenreReview

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

Citations12
Published2024
Admission routes1
Has abstractyes

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