The effect of double W tension-reduced suture technique on the abdominal scars following the da Vinci robot-assisted gastrectomy for severely obese patients
Bibliographic record
Abstract
OBJECTIVE: To analyze the effect of a new type of tension-reduced suture named "double W tension-reduced suture technique" on the abdominal scars following the da Vinci robot-assisted gastrectomy for severely obese patients. METHODS: 40 abdominal incisions following the da Vinci robot-assisted gastrectomy on severely obese patients from September 1st, 2021 to March 1st, 2022 were comprised in the study. 20 incisions were closed by the conventional full-thickness surgical suture as the control group, and 20 incisions were sewn up by double W tension-reduced suture as the double W group. The scars were assessed at the 1-month follow-up visit using the Vancouver scar scale (VSS), ultrasound and patient satisfaction. Meanwhile, digital photographs of scars were taken as well. RESULTS: The VSS score was 6.80 ± 2.16 in the control group, while that of the double W group was 2.60 ± 1.89. The difference between groups was significant. Digital photographs showed that the scar color was not only light and close to the skin color, but also flat and soft in the double W group. Ultrasound showed that the fibers of subcutaneous tissue in the double W group were arranged neatly, the ultrasonic signal intensity was relatively uniform, and the tunnel was small without obvious lacunae. More patients were satisfied and very satisfied with scars in the double W group. CONCLUSION: Double W tension-reduced suture technique could significantly improve the appearance and reduce comorbidities of scars following the da Vinci robot-assisted gastrectomy for severely obese patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".