Intraoperative assessment of diastolic function: utility of echocardiography
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review discusses the current and future applications of different echocardiographic modalities in evaluating diastolic function intraoperatively. RECENT FINDINGS: Normal diastolic function is required for optimal cardiac performance. There is sufficient evidence to support the significant prevalence of preoperative diastolic dysfunction and its incidence following cardiac surgery, however controversy still exists regarding the impact of diastolic dysfunction on adverse outcomes. Echocardiography provides a relatively safe, practical and noninvasive means to evaluate perioperative diastolic function, however conventional measures may be limited by the impact of changes in heart rate, rhythm and loading conditions. Newer echocardiographic modalities are reportedly less sensitive to acute changes in loading conditions, and may therefore complement the use of conventional echocardiographic techniques in the perioperative period. SUMMARY: The availability of effective technology for diagnosing the presence and progression of perioperative diastolic function should assist in the identification of high-risk cardiac surgical patients who may benefit from appropriate triaging and therapeutic intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".