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Diagnosis of Diastolic Dysfunction: Importance of Spectral Doppler Imaging

2002· article· en· W4232594762 on OpenAlexaffabout
André Denault, Françis Bernard, Pierre Couture

Bibliographic record

VenueAnesthesia & Analgesia · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsPQ Corporation (Canada)Montreal Heart Institute
Fundersnot available
KeywordsMedicineCardiologyInternal medicineDiastoleDoppler effectPulmonary veinDoppler echocardiographyHemodynamicsLower limbs venous ultrasonographyMitral valveMitral regurgitationRadiologyVeinBlood pressureAtrial fibrillation

Abstract

fetched live from OpenAlex

In Response: Dr. Poelaert’s comments are of great interest to us and we agree that Doppler pattern can be influenced by loading condition. This is particularly true for transmitral valve velocity pattern, but the pulmonary venous flow is also affected by other factors, including age, heart rate, cardiac output, left ventricular systolic function, and left atrial function (1). In our study, the diagnosis of diastolic dysfunction was based not only on the mitral valve inflow but also on the pulmonary vein velocity signal as suggested by the Canadian Consensus recommendations for the measurement and reporting of diastolic dysfunction by echocardiography (2). The Doppler examination was performed, as we mentioned, “before pericardiotomy during a period of hemodynamic stability.” We routinely evaluate both pulmonary veins and have rarely encountered significant velocity differences before cardiac surgery Doppler signal from the right and left pulmonary veins are similar unless the Doppler signal intensity is attenuated because of improper positioning. The Doppler signal from the pulmonary vein can vary with mechanical ventilation, as we published before (1), but the systolic to diastolic ratio stayed the same. Klein et al. (3) observed discrepancies in velocities in up to 24% of patients with significant mitral regurgitation but in their series, the pulmonary flow was still abnormal in both veins. The use of a loading test (4) was not published when we collected the data and the use of tissue Doppler (5–8) was not a modality available on our transesophageal echocardiographic system at the time of the study. Furthermore, they are not yet part of the Canadian Consensus, which we used in the evaluation of diastolic dysfunction (2). We do agree however that they provide complementary and useful information on the appreciation of diastolic function. Our cardiologist, who was familiar with the use of tissue Doppler, did evaluate the mitral annular displacement in a semiqualitative fashion and could predict and confirm the type of diastolic abnormality based on visual inspection. This mode of evaluation, which is frequently discussed among “diastologists,” had not been formally validated at the time of the study, so we could not mention it in the methodology. André Denault, MD, FRCPC Francis Bernard, MD, FRCPC Pierre Couture, MD, FRCPC

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)0.004
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0100.021
Insufficient payload (model declined to judge)0.0040.004

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 head0.015
GPT teacher head0.227
Teacher spread0.213 · 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 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".

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Citations0
Published2002
Admission routes2
Has abstractyes

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