Importance of Data Granularity in Using eGFR Slope as a Surrogate for DCGF
Bibliographic record
Abstract
Purpose: Minimally invasive techniques have been explored in KT recipients, for early recovery and less pain.The purpose of our study was to analyze the diff erence between minimal-incision kidney transplantation (MIKT), and conventional kidney transplantation (CKT).Methods: Living donor kidney transplant recipients operated between February 2006 to June 2024 at Seoul St. Mary's Hospital were included in our study.100 selected patients who had received MIKT were compared with a 1:4 propensity-score matched CKT group, based on age, sex and BMI.Results: The average age was 29.6±8.3 years in the MIKT group, and 46.1±11.5 years in the CKT group.Female sex was 88.0% in the MIKT group and 32.0 % in the CKT group, and average BMI were 19.6±2.9 kg/m2 and 23.7±3.6 kg/m2 respectively.Other than the number of plasmaphereses being lower in the MIKT group (1.1±2.0 vs. 1.7±2.7),immunologic characteristics and immunosuppression were similar between the two groups.Operation time was signifi cantly shorter in the MIKT group compared with CKT group (256.2±56.9 vs. 279.8±59.4minutes, p<0.001).There were no signifi cant diff erences in delayed graft function, biopsyproven acute rejection, graft failure and all-cause mortality between the two groups.Incidence of long-term complications (infection, malignancy, cardiovascular) did not show a signifi cant diff erence between groups.Conclusions: MIKT is a safe and feasible method with no statistical diff erences in transplant outcomes and complications.MIKT can be a safe and suitable option for appropriate patients, with favorable cosmetic results.
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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 | 0.017 | 0.078 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".