Abstract 11006: Fick’s versus Cardiac MRI Measured Pulmonary Blood Flow in Pre-Fontan Assessment
Bibliographic record
Abstract
Background: Fick’s principle (FP) is unreliable if there are two sources of pulmonary blood flow (Q p ) with different saturations. We compared Q p and pulmonary vascular resistance (PVRI) measured by cardiac MRI (CMR) and FP in pre-Fontan patients. Material and Methods: Retrospective analysis of CMR and catheterization data obtained under the same general anesthetic from Jan 2009 to March 2021. FP derived Q p and PVRI used assumed O 2 consumption from Sackler’s equation. Paired T test was used for comparison between FP and CMR parameters. Results: 98 patients, with (mean ± sd): age 3.01±1.38 years and weight 13.31±2.4 kg. The FP derived Q p was 2.5(± 0.58) L/min/m 2 and PVRI1.91±0.61 Woods units*m 2 (WU*m 2 ). CMR measured pulmonary arterial flow (Q pa ) and pulmonary venous flow (Q pv ) were 1.97±0.55 L/min/m 2 and 3.49±0.76 L/min/m 2 , respectively. The PVRI using CMR Q pv was 1.38±0.47 WU*m 2 . The paired mean differences between Q p by FP and CMR Q pa or CMR Q pv were 0.53 (95%CI 0.41, 0.66; p<0.001) and -0.98(95%CI -1.13, -0.83; p<0.001), respectively. The paired mean difference between PVRI by FP and PVRI by CMR Qpv was 0.53(95%CI 0.44, 0.61; p<0.001). The difference between CMR Q pv and FP derived Q p were correlated with aortopulmonary collateral flow (Pearson r=0.615, p<0.001). PVRI derived from FP (1.91±0.61 WU*m 2 ) was higher than that from CMR (1.38±0.47, p<0.001). However, this only resulted in one FP based PVRI estimate being >4.0WU*m 2 when the CMR PVRI was <4.0WU*m 2 . Conclusion: s: FP derived Q p is lower than CMR Q pv resulting in higher PVRI estimates by FP. In most patients this will not affect clinical risk stratification based on PVRI. In high-risk candidates for the Fontan, CMR based flow measurements are preferrable.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".