Abstract 15805: Longitudinal Changes in Univentricular Patients Pre-Bidirectional Cavopulmonary Connection and Pre-Fontan
Bibliographic record
Abstract
Background: Little is known about serial changes in the physiology of single ventricle (SV) patients during staged palliation and if HLHS patients differ from the rest. Methods: We analyzed SV patients who had a combined cath with CMR at both the preBCPC and preFontan studies from 2016-2019. Flow contrast mapping used to calculate pulmonary arterial (Qpa) and venous (Qpv) flow. Systemic blood flow (Qs) calculated using [superior vena cava (SVC) flow + descending aortic flow at the level diaphragm]. Cerebral vascular resistance (CVRi) calculated using [ascending aortic pressure (AoP)-right atrial pressure (RAP)/SVC flow]. Systemic vascular resistance (SVR) calculated using [(AoP-RaP)/Qs]. Pulmonary vascular resistance (PVR) was calculated using [(mean PAP - LAP)/Qpv]. Results: 30 patients were found, 10 with HLHS. The BCPC unloaded the heart, EDVi fell from preBCPC to preFontan. From preBCPC to preFontan(Table1): PA flow fell, but was compensated by increased APC flow to keep QpQs~1, PApressure and PVRi fell. Compared to others, the HLHS patients had larger hearts (EDVi) and lower PA but higher APC flow at both preBCPC(Table2)and preFontan(Table3). By preFontan, HLHS patients had worse function: higher ESVi, lower EF. Conclusion: QpQs ~1 is maintained by increase in APC flow. HLHS hearts are larger and deteriorate progressively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".