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Record W4283330457 · doi:10.3389/fcvm.2022.907168

Case Report: Posterior Thoracic Window in the Presence of Pleural Effusion in Critical Care Medicine: One More Chance to Image the Aortic Valve

2022· article· en· W4283330457 on OpenAlexaff
Francesca Mantovani, Giovanni Benfari, Andréa Barbieri, Francesco Manca, Vincenzo Guiducci, Alessandro Navazio, Marie‐Annick Clavel

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicinePleural effusionRadiologyRegurgitation (circulation)Pericardial effusionAortic valveCardiologyInternal medicineCardiothoracic surgerySurgery

Abstract

fetched live from OpenAlex

Good quality echocardiographic images in the setting of critical care medicine may be difficult to obtain for many reasons. We present a case of an 85-year-old woman with acute pulmonary edema and pleural effusion, where transthoracic bedside echocardiographic examination raised a suspicion for significant aortic valve disease. However, given the orthopneic decubitus of the patients, the quality of images was poor. To increase the accuracy of diagnosis, a posterior thoracic view through the pleural effusion in the sitting position was used. This view allowed the diagnosis of mixed aortic valve disease (aortic stenosis and regurgitation) and the quantification of valve disease through multiparametric criteria as recommended by current guidelines. The posterior thoracic view, when feasible, may provide a useful option in the assessment of cardiac structures and further diagnostic information in technically difficult echocardiographic examinations.

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)0.007
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 score0.015
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0040.003
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0150.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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
Published2022
Admission routes1
Has abstractyes

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