Relationship of predicted postoperative product to postpneumonectomy cardiopulmonary complications.
Bibliographic record
Abstract
BACKGROUND: This retrospective analytic study was to evaluate diffusing capacity of the lung for carbon monoxide (DLCO) and predicted postoperative product (PPP) as predictors of postpneumonectomy cardiopulmonary complications. METHODS: One-hundred fifty-one patients underwent pneumonectomy at Vancouver General Hospital from January 1992 to December 1997. The PPP was obtained by multiplying the predicted postoperative (ppo) FEV1 by the ppo DLCO, both expressed as % of predicted. The ppo FEV1 and DLCO were derived by calculating the proportional loss of functional lung from the resected lung. We also evaluated a new index, the measured product (MP), obtained by multiplying the measured preoperative FEV1 by DLCO, both expressed as % of predicted. RESULTS: Patients with complications had lower FEV1, lower DLCO, lower MP, lower ppo FEV1, lower ppo DLCO, and lower PPP than patients without complications. DLCO at 70% of predicted was the best predictor of postoperative complications, while PPP at 1400 was similar to MP at 5000 and ppo DLCO at 40% of predicted in predicting postoperative complications. The complication rate was 88% in patients with DLCO < 70% of predicted, compared with a complication rate of 19% in patients with DLCO > or = 70% of predicted (sensitivity = 68%, specificity = 93%), while the complication rate was 66% in patients with PPP < 1400, compared with a complication rate of 25% in patients with PPP > or = 1400 (sensitivity = 63%, specificity = 78%). CONCLUSIONS: DLCO < 70% of predicted is associated with increased risk of complications following pneumonectomy. PPP determined preoperatively allows a patient with a critically low value (< 40% of predicted) for one variable (either ppo FEV1 or ppo DLCO) to be accepted for surgery on the basis of a good value in the other. Patients with PPP > or = 1400 have a relatively low postpneumonectomy complication rate.
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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 | 0.007 |
| 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) | 0.001 | 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".