Role of CO Diffusing Capacity during Exercise in the Preoperative Evaluation for Lung Resection
Bibliographic record
Abstract
We conducted a prospective study to evaluate whether lack of an adequate increase in diffusing capacity for carbon monoxide (DL(CO)) during exercise is associated with a greater postoperative complication rate after lung resection. We used the three-equation method (3EQ-DL(CO)), a modification of the single breath DL(CO) technique to determine DL(CO) during exercise in 57 patients undergoing lung resection at Vancouver General Hospital from October 1998 to May 1999. 3EQ-DL(CO) was determined during steady-state exercise at 35% and 70% of the maximal workload reached in a progressive exercise test. Maximal oxygen uptake (VO(2)max), DL(CO) at rest, and the increase in DL(CO) during exercise were compared in relation to postoperative complications. Patients with complications had lower resting values of DL(CO) (R-DL(CO)), a smaller increase in DL(CO) from rest to 70% of maximal workload expressed as a percent of the predicted DL(CO) at rest ([70% - R]-DL(CO)%), and a lower VO(2)max than did patients without complications. Results suggested that (70% - R)-DL(CO)% was the best preoperative predictor of postoperative complications; a cutoff limit of 10% was the best index to identify complications, yielding a complication rate of 100% in patients with (70% - R)-DL(CO)% < 10% as compared with a complication rate of 10% in patients with (70% - R)-DL(CO)% >/= 10% (sensitivity = 78%, specificity = 100%). Patients who do not increase their DL(CO) sufficiently during exercise ([70% - R]-DL(CO)% < 10%) have a greater complication rate after lung resection.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".