Analysis of pulmonary complications and predicted postoperative pulmonary function in oncologic lung resections
Bibliographic record
Abstract
Background: Postoperative pulmonary complications (PPCs) represent a significant source of morbidity and mortality in surgical patients. Measurement of predicted postoperative forced expiratory volume in the first second (ppo FEV1) may allow for reliable prediction of PPCs and perioperative planning. This study aimed to determine if impaired ppo FEV1 is associated with increased risk of PPCs following oncologic lung resection. Methods: Patients who underwent elective pulmonary resection at The Ottawa Hospital between 2008 and 2018 were evaluated. The presence and severity of PPCs as defined by the Ottawa Thoracic Morbidity & Mortality system were analyzed. The incidence of PPCs was evaluated based on different ppo FEV1 cut-off values (40%, 50%, and 60%), and a multivariable logistic regression was performed to identify predictors of PPCs. Results: Of 1,949 included patients, a thoracoscopic approach (64.4%) was most frequently utilized, and lobectomies represented the most common procedure (60.5%). All cut-off ppo FEV1 values of <40% (P<0.001), <50% (P<0.001), and <60% (P=0.004) were associated with more frequent PPCs (13.0%, 11.6%, and 7.6%, respectively), while only ppo FEV1 <50% showed differences in both minor (P<0.001) and major (P=0.005) PPCs. With ppo FEV1 <50%, differences in PPCs were demonstrated specifically in both thoracoscopic (P=0.03) and open (P=0.003) procedures. On multivariable analysis, ppo FEV1 <50% (P=0.03) and need for operative conversion (P<0.001) independently predicted PPCs. Conclusions: Routine assessment of ppo FEV1 is a practical strategy to identify patients at increased risk of developing PPCs, and can identify candidates for preoperative optimization and postoperative pulmonary support.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".