Relative Lung Perfusion on Ventilation–Perfusion Scans After Double Lung Transplant
Bibliographic record
Abstract
BACKGROUND: Pulmonary blood flow can be assessed on ventilation-perfusion (VQ) scan with relative lung perfusion, with a 55% to 45% (or 10%) right-to-left differential considered normal. We hypothesized that wide perfusion differential on routine VQ studies at 3 mo posttransplant would be associated with an increased risk of death or retransplantation, chronic lung allograft (CLAD), and baseline lung allograft dysfunction. METHODS: We conducted a retrospective cohort study on all patients who underwent double-lung transplant in our program between 2005 and 2016, identifying patients with a wide perfusion differential of >10% on a 3-mo VQ scan. We used Kaplan-Meier estimates and proportional hazards models to assess the association between perfusion differential and time to death or retransplant and time to CLAD onset. We used correlation and linear regression to assess the relationship with lung function at time of scan and with baseline lung allograft dysfunction. RESULTS: Of 340 patients who met inclusion criteria, 169 (49%) had a relative perfusion differential of ≥ 10% on a 3-mo VQ scan. Patients with increased perfusion differential had increased risk of death or retransplantation ( P = 0.011) and CLAD onset ( P = 0.012) after adjustment for other radiographic/endoscopic abnormalities. Increased perfusion differential was associated with lower lung function at time of scan. CONCLUSIONS: Wide lung perfusion differential was common after lung transplant in our cohort and associated with increased risk of death, poor lung function, and CLAD onset. The nature of this abnormality and its use as a predictor of future risk warrant further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".