A Thickness Calibration Device Is Needed to Determine Staple Height and Avoid Leaks in Laparoscopic Sleeve Gastrectomy
Bibliographic record
Abstract
BACKGROUND: Leaks after sleeve gastrectomy (SG) may be due to a mismatch between staple height and tissue thickness. The aim of this study was to determine the range of gastric thicknesses in three areas of stapling. METHODS: SG was performed using a 40-Fr suction calibration system 4 cm from the pylorus. Measurement of combined gastric walls was accomplished with an applied pressure of 8 g/mm(2) on the fundus, midbody, and antrum. RESULTS: We enrolled 26 SG patients (15 women, 11 men; mean age 36.8 years). Body mass index (BMI) averaged 45.3 kg/m(2) overall, 44.7 kg/m(2) for males and 45.7 kg/m(2) for females. Although male patients had a thicker stomach antrum than female patients (3.12 vs. 3.09 mm), the midbody (2.57 vs. 3.09 mm) and proximal areas (1.67 vs. 1.72 mm) were thicker in female patients. However, some maximum fundus thicknesses were up to 2.83 mm in females and 2.28 mm in males. Some antra were as thick as 4.07 mm in females and 5.39 mm in males. Also, men had a longer average staple line (22.95 vs. 19.90 cm). CONCLUSION: Because of the range of gastric thicknesses, a single staple height cannot be used to appose the full range of gastric wall thicknesses without potentially causing necrosis or poor apposition. To help avoid leaks, a thickness calibration device is needed to determine correct staple height.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".