Selection of patients for lung volume reduction surgery using a power law analysis of the computed tomographic scan
Bibliographic record
Abstract
BACKGROUND: A study was undertaken to test the hypothesis that patients respond better to lung volume reduction surgery (LVRS) if their emphysema is confluent and predominantly located in the upper lobes. METHODS: A density mask analysis was used to identify voxels inflated beyond 10.2 ml gas/g tissue (-910 HU) on preoperative and postoperative CT scans from patients receiving LVRS. These hyperinflated regions were considered to represent emphysematous lesions. A power law analysis was used to determine the relationship between the number (K) and size (A) of the emphysematous lesions in the whole lung and two anatomical regions using the power law equation Y=KA(-D). RESULTS: The analysis showed a positive correlation between the change in the power law exponent (D) and the change in exercise (Watts) after surgery (r=0.47, p=0.03). There was also a negative correlation between the power law exponent D in the upper region of the lung preoperatively and the change in exercise following surgery (r=-0.60, p<0.05). CONCLUSIONS: These results confirm that patients with large upper lobe lesions respond better to LVRS than patients with small uniformly distributed disease. Power law analysis of lung CT scans provides a quantitative method for determining the extent and location of emphysema within the lungs of patients with COPD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".