Factors predicting outcome after pulmonary endarterectomy
Bibliographic record
Abstract
OBJECTIVE: Few studies have reported predictive factors of outcome after pulmonary endarterectomy (PEA) in chronic thromboembolic pulmonary hypertension. The purpose of this study was to determine factors influencing mortality and predictors of hemodynamic improvement after PEA. METHODS: A total of 383 consecutive patients who underwent PEA between January 2005 and December 2009 were retrospectively reviewed. Among them, 150 were fully reevaluated 7.5±1 months after PEA by NYHA class, 6-minute walk distance (6MWD), percentage of predicted carbon monoxide transfer factor (TLCO) and right heart catheterisation. RESULTS: Mortality rates at 1 month, 1 year and 3 years were 2.8%, 6.9% and 7.5%, respectively. Preoperative pulmonary vascular resistance (PVR) independently predicted 1-month, 1- and 3-year mortality and age predicted mortality at 1 year and 3 years. Significant improvement in NYHA class and 6MWD were observed and PVR decreased from 773±353 to 307±221 dyn.sec.cm-5 (p<0.001). In 96 patients (64%), PVR decreased by at least 50% and/or was reduced to lower than 250 dyn.sec.cm-5. Preoperative cardiac output (CO) and TLCO predicted hemodynamic improvement. CONCLUSION: PEA is associated with an excellent long-term survival and a marked improvement in clinical status and hemodynamics. Some preoperative factors including PVR, CO and TLCO can predict postoperative outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".