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Record W2786566816 · doi:10.14740/cr637w

Are Measures of Left Ventricular Longitudinal Shortening Affected by Left Atrial Enlargement?

2018· article· en· W2786566816 on OpenAlexvenueno aff
Angel López‐Candales, Dagmar F. Hernández-Suárez, Francisco López Menéndez

Bibliographic record

VenueCardiology Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsCardiologyMedicineInternal medicineLeft atrial enlargementLeft atrial pressureVentricular functionAtrial fibrillationHemodynamicsSinus rhythm

Abstract

fetched live from OpenAlex

BACKGROUND: Even though left atrial (LA) size and function are intimately related to left ventricular (LV) diastolic dysfunction, the role of LA with regard to LV systolic function is less clear. Consequently, we examined the potential association that might exist between measures of longitudinal LV systolic shortening and LA dilation using LA volume index (LAVI). METHODS: In this retrospective analysis, data from 75 echocardiograms (mean age 53 ± 14; range 24 - 89 years; mean body surface area (BSA) 2.0 ± 0.3) were analyzed. RESULTS: Peak global longitudinal (PGLS) correlated best with LV mass index (LVMI) followed by mitral annular systolic excursion (MAPSE), and age. Similar results were obtained when analyzing the best variables that correlated with LAVI. Finally, MAPSE correlated best with PGLS, then with MA tissue Doppler systolic velocity, BSA, and LAVI in that order. All patients had normal LV ejection fraction (LVEF) and normal sinus rhythm when studied. CONCLUSIONS: LAVI does not directly affect LV systolic function and longitudinal measures of LV shortening are mainly dependent on LV mass. Additional studies are now required to determine how these associations vary when different degrees of LV dilatation and systolic dysfunction are included in the analysis.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.104
GPT teacher head0.368
Teacher spread0.265 · 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 designObservational
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

Citations4
Published2018
Admission routes1
Has abstractyes

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