Predicted Postoperative Product and Diffusion Heterogeneity Index in the Evaluation of Candidates for Lung Resection
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this retrospective study was to evaluate whether abnormal predicted postoperative variables and predicted postoperative product are useful in predicting postoperative complications. The secondary objective was to assess whether an abnormal diffusion heterogeneity index is associated with increased postoperative complications. METHODS: In this retrospective study we evaluated the medical records of 57 patients who underwent lung resection for lung cancer. Calculations of the predicted postoperative variables were done using preoperative testing data, including the extent of the resected lung segments. Predicted postoperative product was obtained by multiplying the predicted postoperative percent-of-predicted FEV(1) by the predicted postoperative percent-of-predicted single-breath diffusing capacity of the lung for carbon monoxide (D(LCO)). The measured product was obtained by multiplying FEV(1) by D(LCO). We derived diffusion heterogeneity index from measurements of the single-breath D(LCO) with the 3-equation method, as a measure of the heterogeneity of the distribution of gas exchange in the lung. RESULTS: Patients with complications had lower predicted postoperative FEV(1) (P < .001), lower predicted postoperative D(LCO) (P < .001), lower predicted postoperative maximal oxygen uptake (P < .001), lower predicted postoperative increase in percent-of-predicted D(LCO) at 70% work load from at-rest percent-of-predicted D(LCO) (ΔD(LCO)%) (P < .001), lower predicted postoperative product (P < .001), and lower measured product (P = .004). Interestingly, diffusion heterogeneity index increased with exercise in [corrected] patients with complications but decreased with exercise in [corrected] patients without complications. CONCLUSIONS: The predicted postoperative variables, predicted postoperative product, measured product, and diffusion heterogeneity index are potentially useful predictors of complications in candidates for 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 | 0.000 | 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".