New or enlarging hiatal hernias after thoracic surgery for early lung cancer
Bibliographic record
Abstract
Objective The study objective was to determine the relationship between lung resection and the development of postoperative hiatal hernia. Methods Preoperative and postoperative computed tomography imaging from 373 patients from the International Early Lung Cancer Action Program and the Initiative for Early Lung Cancer Research on Treatment were compared at a median of 31.1 months of follow-up after resection of clinical early-stage non–small cell lung cancer. Incidence of new hiatal hernia or changes to preexisting hernias were recorded and evaluated by patient demographics, surgical approach, extent of resection, and resection site. Results New hiatal hernias were seen in 9.6% of patients after lung resection (5.6% after wedge or segmentectomy and 12.4% after lobectomy; P = .047). The median size of new hernias was 21 mm, and the most commonly associated resection site was the left lower lobe (24.2%; P = .04). In patients with preexisting hernias, 53.5% demonstrated a small but significant increase in size from 21 to 22 mm ( P < .0001). All hernias persisted through the latest postoperative computed tomography scan. When 110 surgical patients without preexisting hernia were matched by sex, age, and smoking to nonoperative controls, the incidence of new hernia at follow-up was significantly higher among those who underwent surgery (17.3% vs 2.7%, P = .0003). Conclusions Both open and minimally invasive lung resection for clinical early-stage lung cancer are associated with new or enlarging postoperative hiatal hernia, especially after resections involving the left lower lobe.
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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 | 0.000 | 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 | 0.001 |
| 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".