Effects of different tidal volumes for one-lung ventilation on oxygenation with open chest condition and surgical manipulation: a randomised cross-over trial.
Bibliographic record
Abstract
BACKGROUND: The ideal tidal volume (TV) during one-lung ventilation (OLV) remains controversial. High TVs may increase the incidence of postoperative lung injury after thoracic surgery. There is nonetheless little evidence that the use of low TV during OLV will fail to provide adequate arterial oxygenation. We evaluated the influence of low (5 mL/kg-1) and high (10 mL/kg-1) TV on arterial oxygenation during one-lung ventilation in clinical conditions. METHODS: A hundred patients scheduled for lung surgery were studied. Patients were randomly assigned to either 30 minutes of one-lung ventilation with a TV of 10 mL/kg-1 at a rate of 10 breaths/minute (Group 10, N.=50) or a TV of 5 mL/kg-1 with 5 cmH2O PEEP at a rate of 20 breaths/minute (Group 5, N.=50). According to the rules of crossover design during the subsequent 30 minutes, each patient received the alternative management. Arterial blood partial pressures, hemodynamic responses, and ventilatory parameters were recorded. Results are presented as means ± SDs; P<0.05 was considered statistically significant. RESULTS: PaO2 was unaffected by TV (10 mL/kg-1: 218±106 versus 5 mL/kg-1: 211±119 mmHg, P=0.29). Calculated intrapulmonary shunt fraction was also similar with each TV during OLV (5 mL/kg-1: 25±9% versus 10 mL/kg-1: 24±8%, p=0.14). In contrast, low TV significantly increased PaCO2 (10 mL/kg-1: 39±6 versus 5 mL/kg-1: 44±8 mmHg, P<0.001). There were significant differences both in peak (10 mL/kg-1: 27±6 versus 5 mL/kg-1: 21±5 cmH2O, P<0.001) and plateau airway pressure values (10 mL/kg-1: 22±6 versus 5 mL/kg-1: 18±5 cmH2O, P<0.001) during OLV. CONCLUSION: Low TV (5 mL/kg-1) accompanied by 5 cmH2O PEEP provides comparable arterial oxygenation and intrapulmonary shunt fraction during one-lung ventilation as higher TV (10 mL/kg-1) without PEEP.
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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) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| 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".