Minimally invasive approach to thoracic effusions in patients with ventricular assist devices
Bibliographic record
Abstract
The aim of this study was to compare our experience between open and video-assisted thoracic surgery (VATS) approaches to the management of thoracic effusions in ventricular assist device (VAD) patients. This was a retrospective review of a prospectively collected database of VAD patients at a single institution. Patients who were operated on for pericardial and/or pleural effusions were included. Primary outcomes included operative mortality and morbidity as well as effusion recurrence. From 1993 to 2009, 360 adult patients underwent VAD placement. Twenty-three patients (11.9%) required operative management of pleural (n = 24), pericardial (n = 13) or both pleural and pericardial (n = 6) effusions [open = 20 (47%); VATS = 23 (53%)]. Drainage with decortication was performed in five patients, with the remaining undergoing drainage alone. Open and VATS patients were similar in age, gender and indication for VAD support. Conversion from VATS to open was necessary in four patients (17%). There was no operative mortality and no difference in perioperative complications between approaches. The open and VATS approaches had similar rates of pleural (open = 63%; VATS = 41%; P = 0.42) and pericardial (open = 31%; VATS = 17%; P = 1) effusion recurrences. In spite of apparent challenges, the VATS approach may be as safe and effective as open surgery for the management of pleural and pericardial effusions in VAD patients in centres with significant minimally invasive thoracic experience.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".