A new index measured by cardiovascular magnetic resonance imaging to detect mechanical heart valve malfunction
Bibliographic record
Abstract
More than two thirds of valve replacement operations performed each year used mechanical heart valve. These valves are subject to complications such: pannus and/or thrombus formation. One other potential complication is a malfunction in one of the valve leaflets. It is then important to develop parameters that will allow a non-invasive diagnosis of such valve malfunction. In the present study, we evaluated under steady low flow (1-8 L/min) and pulsatile flow (3, 5 and 7 L/min) a bileafleat mechanical heart valve with normal function, 50% and 100% of one valve leaflet malfunction. Image analysis was performed using cardiovascular magnetic resonance imaging to evaluate transvalvular pressure gradients (TPG), effective orifice area and a new index given by central/lateral velocity ratio downstream of the valve. Our results showed that the flow upstream and downstream of the defective valve is highly influenced by malfunction severity. TPG did not allow differentiating valve malfunction at low flow under steady and pulsatile conditions. However the new index given by central/lateral ratio allowed differentiating the presence of valve malfunction using a single transverse velocity measurement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".