Individualized parameters for mechanical ventilation during thoracic operations: Optimizing respiratory support
Bibliographic record
Abstract
Introduction: Adequate respiratory support with mechanical lung ventilation (MLV) is crucial for maintaining gas exchange and pulmonary circulation hemodynamics in patients with severe lung diseases in the perioperative period. However, the selection of optimal parameters for ventilation is often a serious problem, which can lead to the development of complications and worsening of treatment outcomes. Purpose: This study aimed to evaluate the effectiveness of the developed method of individual calculation of ventilator parameters to optimize respiratory support in patients with various lung diseases undergoing surgical intervention. Methods: This study used a prospective clinical approach to optimize mechanical lung ventilation by calculating individualized ventilatory parameters based on each patient's lung function during surgery. Results: The results showed that in patients with unilateral lesions, the application of the developed method achieved PaO2 94.1±6.7 mmHg and PaCO2 36.2±4.5 mmHg, mean pulmonary artery pressure 25.8±3.6 mmHg, as well as cardiac output 4.8±0.8 l/min and oxygen transport 489±77 ml/min at the final post-operative stage. Even in bilateral diffuse lesions, individualized ventilatory parameters provided PaO2 79.6±11.3 mmHg and reduced bronchial resistance to 11.4±3.6 cmH2O/l/sec after surgery. Despite gross respiratory dysfunction, the personalized approach maintained PaO2 79.2±9.7 mmHg and PaCO2 46.1±6.3 mmHg postoperatively in patients with congenital pulmonary malformations such as cystic hypoplasia. Conclusion: This study demonstrates the high efficacy of personalized approaches to respiratory support management to improve patient outcomes and reduce the risk of complications in patients with lung disease in the perioperative period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".