THE LEARNING CURVE OF ROBOTIC-ASSISTED LAPAROSCOPIC PARTIAL NEPHRECTOMY: A SINGLE CENTRE'S 6-YEARS EXPERIENCE
Bibliographic record
Abstract
Seroma and skin necrosis are potential complications following abdominoplasty. Many methods have been employed to prevent these complications, including the progressive tension suture technique.The authors evaluate a progressive tension suture technique modification using the Quill barbed suture (Angiotech Pharmaceuticals, Inc., Vancouver, British Columbia, Canada) to determine whether the original benefits of this classic technique can be obtained in a shorter operative period.The modified progressive tension closure technique with Quill sutures uses barbed sutures to plicate the abdominoplasty flap to the underlying abdominal wall. The placement of the suture is performed with a running suture technique and provides progressive tension, resulting in minimal tension along the incision line. Data from 58 patients undergoing abdominoplasty using this technique are examined, including time to insert the sutures and complications such as seroma, hematoma, and skin necrosis.There was a marked reduction in the time necessary to perform the modified progressive tension suture technique using barbed sutures compared to previously published data. The authors' average time was nine minutes to complete plication of the entire abdominal flap. One seroma is reported, which was resolved with one aspiration. No hematomas or skin necrosis complications are reported.Using barbed sutures to perform progressive tension suture closure in abdominoplasty is a safe and effective way to considerably reduce operative time and retain all of the benefits of the original progressive tension suture technique.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".