Assessment of preoperative noninvasive ventilation before lung cancer surgery: The preOVNI randomized controlled study
Bibliographic record
Abstract
OBJECTIVES: The preOVNI study was a randomized, controlled, open-label study that investigated whether preoperative noninvasive ventilation (NIV) could reduce postoperative complications after lung cancer surgery. METHODS: Adult patients with planned lung cancer resection and with at least 1 cardiac or respiratory comorbidity were included and randomly assigned to preoperative NIV (at least 7 days and 4 h/day) or no NIV. The primary endpoint was the rate of postoperative protocol-defined complications. RESULTS: Three hundred patients were included. In the NIV group, the median NIV duration was 8 days. No difference of postoperative complication rates was evidenced: 42.6% in NIV group and 44.8% in no-NIV group (P = .75). The rate of pneumonia was greater in no-NIV group compared with the NIV group, but statistical significance was not achieved (28.0 vs 37.7%, respectively; P = .08). The type of surgery (open or minimally invasive) did not impact these results after multivariable analysis. CONCLUSIONS: No benefit was evidenced for preoperative NIV before lung cancer surgery. Further studies should determine the optimal perioperative management to decrease the rate of postoperative complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".