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Record W2929661063 · doi:10.1186/s13244-019-0695-9

Pathophysiology of right ventricular failure in acute pulmonary embolism and chronic thromboembolic pulmonary hypertension: a pictorial essay for the interventional radiologist

2019· review· en· W2929661063 on OpenAlexaff
Yolanda Bryce, Rocío Pérez-Johnston, Errol B. Bryce, Behrang Homayoon, Ernesto Santos-Martin

Bibliographic record

VenueInsights into Imaging · 2019
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Cancer Institute
KeywordsMedicinePulmonary embolismPathophysiologyCardiologyInternal medicinePulmonary hypertensionAfterloadVentricleHeart failureRight ventricular failureInterventional radiologyNeuroradiologyPulmonary arteryRadiologyNeurology

Abstract

fetched live from OpenAlex

Pulmonary embolus (PE) is the third most common cause of cardiovascular death with more than 600,000 cases occurring in the USA per year. About 45% of patients with acute PE will have acute right ventricular failure, and up to 3.8% of patients will develop chronic thromboembolic pulmonary hypertension (CTEPH) with progressive, severe, chronic heart failure. The right ventricle (RV) is constructed to accommodate a low-resistance afterload. Increases in afterload from acute massive and submassive PE and CTEPH may markedly compromise the RV function leading to hemodynamic collapse and death. The purpose of this educational manuscript is to instruct on the pathophysiology of RV failure in massive and submassive PE and CTEPH. It is important to understand the pathophysiology of these diseases as it provides the rationale for therapeutic intervention by the Interventional Radiologist. We review here the pathophysiology of right ventricular (RV) failure in acute massive and submassive PE and CTEPH.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.032
GPT teacher head0.326
Teacher spread0.295 · 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
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

Citations90
Published2019
Admission routes1
Has abstractyes

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