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Record W4402391215 · doi:10.1097/mop.0000000000001385

Diastolic dysfunction: assessment and implications on the single ventricle circulation

2024· review· en· W4402391215 on OpenAlexaff
Ahmed Ali Hassan, Alexander Van De Bruaene, Mark K. Friedberg

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineVentricleDiastoleCardiologyInternal medicineCirculation (fluid dynamics)Blood pressure

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Patients with a functionally single ventricle (SV) are palliated with a series of procedures leading to a Fontan circulation. Over the life span, a substantial proportion of SV patients develop heart failure that can arise from circulatory or ventricular failure. Diastolic dysfunction (DD) is an important determinant of adverse outcomes in SV patients. However, assessment and categorization of DD in the SV remains elusive. We review recent literature and developments in assessment of DD in the SV and its relation to clinical outcomes. RECENT FINDINGS: DD is prevalent in the SV and associated with worse outcomes. Occult DD can be exposed with provocative testing by exercise or preload challenge during catheterization. Likewise, sensitivity to detect DD may be increased via assessment of atrial function and strain imaging. Recent studies revisiting previous concepts such as incoordinate diastolic wall motion show that these are associated with SV end-diastolic pressures and post-Fontan recovery, yielding accessible DD assessment. Emerging technologies such as ultrafast ultrasound (UFUS) can provide noninvasive assessment of myocardial stiffness, inefficient diastolic flow patterns and intraventricular pressure gradients, thereby yielding new tools and insights into diastolic myocardial and hemodynamic properties. SUMMARY: Characterizing DD in the SV continues to have substantial limitations, necessitating synthesis of multiple parameters into an overall assessment, accounting for their change over time, and in the context of the patient's clinical status. New and emerging techniques may help advance DD assessment and the ability to track response to treatment of new targets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.972
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.150
GPT teacher head0.413
Teacher spread0.263 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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