Negative extra-abdominal pressure (NEXAP)-based lung recruitment maneuver versus standard lung recruitment maneuver in the treatment of postoperative atelectasis after cardiac surgery: A single-center randomized controlled trial
Bibliographic record
Abstract
Objective To evaluate the effect of negative extra-abdominal pressure (NEXAP)-based lung recruitment maneuver (LRM) for atelectasis after cardiac surgery, and compare it with stepwise positive end-expiratory pressure (PEEP)-based LRM. Methods In this single-center randomized controlled clinical trial, patients were assigned to the NEXAP or PEEP groups. The primary outcome was the lung ultrasound score (LUSS) on global (LUSStot) and regional (LUSSp, posterior, LUSSa, anterior and LUSSl, lateral regions). Results 29 patients in the NEXAP group and 33 patients in the PEEP group were analyzed. The LUSStot was significantly decreased after LRM in both the NEXAP group (20.7 ± 3.2 vs. 15.6 ± 3.3; p < 0.001) and PEEP group (21.5 ± 4.2 vs. 17.1 ± 4.6; p < 0.001), and ΔLUSStot was significantly greater in the NEXAP group than PEEP group (−5.1 ± 2.3 vs. −3.8 ± 2.2, p = 0.020). Regional LUSS showed that NEXAP reduced LUSSl and LUSSp. PaO 2 /FiO 2 , PaO 2 , Vt, and Crs were significantly improved in the two groups. Conclusion NEXAP is an effective treatment for atelectasis after cardiac surgery, which significantly reduced patients' LUSS, improved pulmonary ventilation (especially in the lateral and posterior regions). NEXAP can further reduce LUSStot than the traditional PEEP-based LRM. Regional LUSS analyses reflecting the different mechanisms between the two methods of LRMs may require 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".