Different Biologic Grafts for Diaphragmatic Crura Reinforcement During Laparoscopic Repair of Large Hiatal Hernia: A Six-Year Single Surgeon Experience
Bibliographic record
Abstract
Background: Large hiatal hernias represent a challenge for surgeons. Biologic grafts are currently popular for the strengthening of crural closure during laparoscopic repair. This study is a retrospective review of crural reinforcement in laparoscopic repair of large hiatal hernias using various biologic grafts performed by a single surgeon in a rural community hospital. Methods: Eleven (n = 11) patients underwent laparoscopic repair of large hiatal hernia in a rural community hospital by a single surgeon from 2009 to 2015. Standard laparoscopic hiatal hernia repair was performed. Different biologic grafts were used for crural reinforcement, including “AlloMax” , “Permacol” , and “Acell MatriStem” . Perioperative data and outcomes of surgery were evaluated. Results: There were six females and five males, with a mean age of 55.4 years and a mean body mass index of 32.5. Eight patients had type III hiatal hernia, two patients had type IV, and one patient had type II. Mean operative time was 244.6 minutes, and mean length of stay was 3.3 days. Mean size of herniated stomach in the chest was 62%. Mean size of the hiatal defect was 7.7 × 6.4 cm. One perioperative complication (9%) included bleeding from left gastric artery. Early complications included shortness of breath (18%), parapneumonic effusion (18%), and early dysphagia (18%). Late complications included persistent gastroesophageal reflux (9%), gastroparesis (9%), and persistent dysphagia (9%). Radiological recurrence was 18% and clinical recurrence was 9% at mean follow-up of 15 months. Conclusions: Laparoscopic repair of large hiatal hernia could be safely performed in rural community hospitals. The choice of the biologic graft, if one is used, should be at the discretion of the surgeon. The cost and availability of the biologic graft are important in decision-making. J Curr Surg. 2016;6(1):6-13 doi: http://dx.doi.org/10.14740/jcs287w
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".