Use of lung-protective strategies during one-lung ventilation surgery: a multi-institutional survey
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Limited evidence suggests that intraoperative lung-protective ventilation (LPV) during one-lung ventilation (OLV) may reduce respiratory complications after thoracic surgery. Little is known about LPV practices during OLV. Our purpose was to assess the state of practice/perspectives of anesthesiologists regarding LPV during elective OLV. METHODS: We conducted a multi-institutional cross-sectional survey of anesthesiologists performing OLV at high-volume Canadian tertiary/university centers. The survey was designed, refined and distributed by a multi-disciplinary team using the Dillman method. Univariable and multivariable analyses were used. RESULTS: Seventy-five (63%) of 120 eligible respondents completed the survey. Although the critical care literature focuses on minimizing tidal volume (TV) as the central strategy of LPV, most respondents (89%, n=50/56) focused on minimizing peak airway pressure (PAP) as their primary strategy of intraoperative LPV. Only 64% (n=37/58) reported actively trying to minimize TV. While 32% (n=17/54) were unsure about the current evidence regarding LPV, 67% (n=36/54) believed that the evidence favoured their use during OLV. Perceived clinical and institutional barriers were the only predictors of reduced attempts to minimize TV on univariate analyses. In multivariable/adjusted analyses, perceived institutional barriers were the only predictors of reduced attempts to minimize TV with adjusted odds ratio of 0.1 (95% CI: 0.03-0.6). CONCLUSIONS: Most anesthesiologists defined low PAP as the primary strategy of LPV during OLV and attempted to minimize it. This study is the first to assess the practice/perspectives of anesthesiologists regarding LPV during OLV and also the first to explore predictors of LPV use. Randomized trials are currently ongoing. However, this study suggests that institutional barriers may subvert future knowledge translation and need to be addressed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 it