Abstract 13158: Improving Clinical Outcomes by Implementing a Multi-Disciplinary Pulmonary Vein Stenosis Team
Bibliographic record
Abstract
Introduction: Pulmonary vein stenosis (PVS) is a rare disease that is associated with high mortality. In 2015, we instituted a multidisciplinary PVS team and surveillance protocol to aid disease detection and treatment. Hypothesis: Implementation of a PVS team will improve clinical outcomes in children with PVS Methods: In this retrospective study (2009-2019), we compared patient characteristics, disease surveillance and treatment practices for primary PVS patients prior to the introduction of the PVS team (NoPVT, first intervention between 2009-2014) and after the introduction of the PVS team (PVT, first intervention between 2015-2019). Results: In our cohort, there were 29 patients in the NoPVT group and 33 patients in the PVT group. We found no significant difference between the median age of first intervention (0.48 (0.22-0.92) years, NoPVT vs 0.66 (0.5-1.1) years, PVT), presence of bilateral disease (34%,NoPVT vs 65%, PVT) and number of veins diseased at diagnosis (2.1 ± 0.15 veins, NoPVT vs 2.3± 0.18 veins PVT). Following pulmonary vein interventions, more patients had surveillance axial imaging after introduction of the PVS team (28% NoPVT vs 72% PVT; p<0.05), with an average number of surveillance scans increased per patient (1.2± 0.46 NoPVT vs 2.4±0.33 PVT, p=0.05). There was an increased number of reinterventions for disease recurrence (17% NoPVT vs 42% PVT; p=0.05). Freedom from death at 1 and 2 years was 56% and 52% in the NoPVT cohort and 83% and 70% in the PVT cohort (p=0.068). Conclusions: Adoption of a surveillance protocol and dedicated PVS team has the potential to improve outcomes in children with primary PVS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".