Can preoperative computed tomography of the chest predict completeness of the major pulmonary fissure at surgery?
Bibliographic record
Abstract
BACKGROUND: An incomplete major pulmonary fissure can make anatomic lung resection technically more difficult and may increase the risk of complications, such as prolonged postoperative air leak. The objective of this study was to determine if preoperative computed tomography (CT) of the chest could accurately predict the completeness of the major pulmonary fissure observed at the time of surgery. METHODS: From October 2008 to June 2009, patients at a single university institution were enrolled if they underwent surgery for a pulmonary nodule, mass or known cancer. At the time of surgery, completeness of the major pulmonary fissure was graded 1 if pulmonary lobes were entirely separate, 2 if the visceral cleft was complete with an exposed pulmonary artery at the base with some parenchyma fusion, 3 if the visceral cleft was only evident for part of the fissure without a visible pulmonary artery and 4 if the fissure was absent. The preoperative CT scan of each patient was graded by a single, blinded chest radiologist using the same scale. We used the Pearson χ2 test with 2-tailed significance to test the independence of the operative and radiologic grading. RESULTS: In 48% (29 of 61) of patients, the radiologic and operative grading were the same. Of those graded differently, 94% (30 of 32) were within 1 grade. Despite this agreement, we observed no statistically significant correlation between the operative and radiologic grading (p = 0.24). CONCLUSION: The major fissure can often be well-visualized on a preoperative CT scan, but preoperative CT cannot accurately predict the completeness of the major pulmonary fissure discovered at surgery.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".