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Record W2891402598 · doi:10.14740/jocmr3513w

Pulmonary Artery Pseudoaneurysms in the Setting of Gout Polyarthropathy: A Case Report

2018· article· en· W2891402598 on OpenAlexvenueno aff
Amelia Bai, Ling Lan, Karl Poon, Varghese Pynadath Joseph, Rachael L. O’Rourke, Khurrum Aftab

Bibliographic record

VenueJournal of Clinical Medicine Research · 2018
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePseudoaneurysmPulmonary arteryHemothoraxDifferential diagnosisRadiologyGoutAneurysmSurgeryInternal medicinePathologyPneumothorax

Abstract

fetched live from OpenAlex

Pulmonary artery pseudoaneurysm is a dangerous pathology often missed as a differential and on imaging. With a 50% mortality rate early detection and intervention are critical to patient outcome, rendering diagnosis based on clinical grounds to be of paramount importance. A high index of suspicion should be placed on patients presenting with unprovoked hemoptysis, hemorrhage or hemothorax. Knowledge of the common causes of pulmonary artery pseudoaneurysms is also important; however, it is integral to note spontaneous occurrences in patients with no known risk factors. Herein, we report a case of an unprovoked pulmonary artery pseudoaneurysm in a 72-year-old male with no known risk factors or causes for the pathology. Additionally, we review the pathophysiology behind the potential association of polyarthritic gout as a cause of pulmonary artery pseudoaneurysms. To the best of our knowledge this is the first reported link between the two diseases, providing grounds to widen literature and increase diagnoses of pulmonary artery pseudoaneurysms.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0040.003
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.218
GPT teacher head0.549
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2018
Admission routes1
Has abstractyes

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