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Record W1552055758 · doi:10.5772/27744

A Pathophysiological Approach to Understanding Pulmonary Hypertension in Cardiac Surgery

2012· book-chapter· en· W1552055758 on OpenAlexaff
Anne Quynh-Nhu Nguyen, Alain Deschamps, France Varin, P. Louis, Y. Andr

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsPathophysiologyMedicinePulmonary hypertensionCardiac surgeryIntensive care medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary hypertension (PH) is associated with increased morbidity and mortality and is an important prognostic factor in cardiac surgery. As the average age and associated comorbidities of cardiac surgical patients increase, the prevalence of PH is likely to rise. In this chapter, we will define PH, classify it on the basis of pathophysiological etiology, and suggest treatment therapies according to this classification. The importance of PH in cardiac surgery, its relationship to right ventricular dysfunction and preventive therapies will also be discussed. When applicable, we will draw from our clinical experience with PH to suggest strategies for the prevention of possible complications.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.052

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 studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0150.008

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 head0.131
GPT teacher head0.282
Teacher spread0.151 · 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
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
Published2012
Admission routes1
Has abstractyes

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