Super-Minimal Incision Technique in Pediatric Kidney Transplantation: A Paired Kidney Analysis
Bibliographic record
Abstract
Background Recently, the demand for minimally invasive techniques in kidney transplantation (MIKT) has increased. However, there is only a limited number of studies on MIKT, especially in pediatric kidney transplants. Hence, we evaluated whether there is a difference between the super-minimal incision technique in pediatric kidney transplantation (SMIPKT) and conventional kidney transplantation (CKT). Methods Between December 2018 and November 2021, 34 patients who underwent pediatric kidney transplantation with a follow-up of 1 month were enrolled. A paired kidney analysis was performed to minimize donor variability and bias. The SMIPKT and CKT groups included 17 patients. Results There was no difference in baseline clinical characteristics, including age, sex, the donor/ recipient weight ratio (DRWR), choice of dialysis modality, pretransplant dialysis time, BMI, renal artery number, cause of ESRD, DGF, length of the kidney and cold ischemic time, tacrolimus concentration at 3 and 7 days, serum creatinine at 1 month and postoperative complication rate between the SMIPKT and CKT groups (all P > 0.05). However, the length of the incision, operation time, intraoperative bleeding, postoperative drainage volume within 24 h and Vancouver scar scale at 1 month were statistically significant (all P < 0.05). Conclusion Compared with CKT, our results indicated that SMIPKT showed more satisfactory cosmetic results, shorter SMIPKT operating time, and reduced intraoperative bleeding and postoperative drainage volume within 24 h. There were also no statistical differences in postoperative complications. Hence, we suggest that SMIPKT is an appropriate method for pediatric kidney transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".