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Record W4389608467 · doi:10.1111/jch.14747

Multiple radiologist review of adrenal CT still frequently misses lateralized surgical primary aldosteronism

2023· article· en· W4389608467 on OpenAlexaff
Darren Mah, Mark Kneteman, Stefan Przybojewski, Vamshi Kotha, Gregory Kline, Alexander A. C. Leung, C. So

Bibliographic record

VenueJournal of Clinical Hypertension · 2023
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrimary aldosteronismMedicineRadiologyAdrenalectomyComputed tomographySurgeryBlood pressure

Abstract

fetched live from OpenAlex

Patients with primary aldosteronism (PA) have increased morbidity and mortality compared to those with essential hypertension. Accurate detection of lateralized PA is important so that affected patients can receive potentially curative adrenalectomy. However, around 40% of patients with lateralized PA have "normal" adrenal glands on computed tomography (CT). Additional independent review of imaging has been shown to improve diagnostic accuracy in many areas of imaging. Therefore, the authors sought to establish if multi-reader re-assessment of previously reported normal CT scans would result in increased detection of surgically remediable disease. The authors found that re-assessment of CT imaging by one, two, or three additional radiologists (or a combination thereof) slightly increased the detection of lateralized disease, but these differences were not statistically significant (p > .05). Readers had low inter-observer agreement (kappa = 0.17). If detection of a discrete nodule on CT was made a prerequisite for adrenal vein sampling (AVS), a second read by another reviewer would still result in an excess of missed cases (84.2%, 36.8%, and 65.8%, respectively, for each of the three independent reviewers). Therefore, a "normal" CT does not preclude the possibility of lateralized PA. Adrenal vein sampling should still be strongly considered wherever available and whenever surgery is considered for treatment of PA, irrespective of CT findings.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.419
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 head0.143
GPT teacher head0.404
Teacher spread0.261 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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