Hiatal hernia after robotic-assisted coronary artery bypass graft surgery
Bibliographic record
Abstract
BACKGROUND: The aim of the present study is to determine the incidence/progression of hiatal hernia (HH) after robotic-assisted coronary artery bypass grafting (RA-CABG) surgery. METHODS: We reviewed the pre- and post-operative computed tomography (CT) of 491 patients who underwent RA-CABG between 2000 and 2017. Post-operative CT was acquired prospectively in a research protocol. CT was reviewed to assess the presence and the size of HH. RESULTS: We found 444/491 (90.4%) had pre-operative CT, while 201/491 (40.9%) had post-operative CT. In total, 155/491 (31.6%) had both pre- and long-term post-operative CT with a mean follow-up of 6.2 (±3.5) years. HH was more prevalent on post-operative CT, 64/155 (41.3%) compared to pre-operative CT, 44/155 (28.4%), P<0.0001. The diameter of pre-existing HH 2.8 (±1.8) cm was significantly greater after surgery 3.9 (±2.5) cm, P<0.0001. As well the volume of the pre-existing HH 5.8 (4.4-9.2) mL (quartile) was significantly greater after surgery 14.1 (7.2-64.9) mL, P<0.0001. 20/155 (12.9%) had a newly developed HH after RA-CABG. A binary multivariate regression including HH risk factors showed that male gender is a predictor of developing a HH after RA-CABG with Hazard Ratio of 3.038, confidence interval (1.10-8.43), P=0.033. CONCLUSIONS: RA-CABG is associated with an increased risk of developing HH and increases the size of pre-existing HH.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".