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Record W4412610294 · doi:10.1016/j.jacadv.2025.101964

Macrovascular Hemodynamics and Peripheral Perfusion in Cardiogenic Shock

2025· review· en· W4412610294 on OpenAlexaff
Leah Kosyakovsky, William Earle, Colter Wichern, Carla Boyle, Conrad Macon, Rebecca Mathew, Benjamin Hibbert, Joaquin E. Cigarroa, Jeffrey A. Marbach

Bibliographic record

VenueJACC Advances · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Ottawa
FundersOregon Health and Science University
KeywordsCardiogenic shockPeripheralCardiologyHemodynamicsInternal medicinePerfusionMedicineShock (circulatory)Myocardial infarction

Abstract

fetched live from OpenAlex

Despite significant advances in care over the past few decades, mortality among patients with cardiogenic shock (CS) remains up to 50%. Given the persistently high mortality, there is an urgent need for both better prognostic tools and treatment strategies. The pathophysiology of CS has major contributions from both macrovascular and microvascular dysfunction, but therapies are titrated toward the more readily measurable metrics (ie, mean arterial pressure, cardiac index, etc) under the assumption that both macrovascular and microvascular dynamics will respond to intervention in tandem. However, emerging evidence suggests that macrovascular and microvascular circulatory functions are not always aligned, particularly in those with critical illness. This review summarizes the significance of different macrovascular and microvascular metrics in CS, drawing from a robust field of evidence to demonstrate the promising role that microvascular tissue perfusion markers play in management of patients with CS and summarize the current understanding of this burgeoning field.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.320
Teacher spread0.312 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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