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Record W3131003364 · doi:10.15420/icr.2020.17

Exercise Right Heart Catheterisation in Cardiovascular Diseases: A Guide to Interpretation and Considerations in the Management of Valvular Heart Disease

2021· review· en· W3131003364 on OpenAlexaff
Felipe Homem Valle, Basma Mohammed, Stephen P. Wright, Robert F. Bentley, Neil Fam, Susanna Mak

Bibliographic record

VenueInterventional Cardiology Reviews Research Resources · 2021
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of TorontoUniversity Health NetworkSt. Michael's HospitalMount Sinai Hospital
Fundersnot available
KeywordsMedicineCardiologyHemodynamicsInternal medicinevalvular heart diseaseCardiac outputCoronary artery diseasePulmonary arteryHeart diseasePulmonary hypertensionIntensive care medicine

Abstract

fetched live from OpenAlex

The use of exercise right heart catheterisation for the assessment of cardiovascular diseases has regained attention recently. Understanding physiologic haemodynamic exercise responses is key for the identification of abnormal haemodynamic patterns. Exercise total pulmonary resistance >3 Wood units identifies a deranged haemodynamic response and when total pulmonary resistance exceeds 3 Wood units, an exercise pulmonary artery wedge pressures/cardiac output slope >2 mmHg/l/min indicates the presence of underlying exercise-induced pulmonary hypertension related to left heart disease. In the evolving field of transcatheter interventions for valvular heart disease, exercise right heart catheterisation may objectively unmask symptoms and underlying haemodynamic abnormalities. Further studies are needed on the use of the procedure to inform the selection of patients who might receive the most benefit from transcatheter interventions for valvular heart diseases.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.009

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 head0.112
GPT teacher head0.448
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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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