Extracapsular versus intracapsular allograft nephrectomy: impact on allosensitization and surgical outcomes
Bibliographic record
Abstract
Introduction: Our objective was to compare the impact of extracapsular(ECAN) versus intracapsular allograft nephrectomy (ICAN)on allosensitization and surgical outcomes.Methods: Between 1990 and 2004, 96 allograft nephrectomieswere performed at our institution. Of these, 29 procedures wereperformed within 1 month of the transplant and were thereforeomitted from analysis. Overall, the results of 44 ECAN and 23ICAN were reviewed.Results: The mean operative times were 110.9 versus 130.4 minfor ICAN versus ECAN (p = 0.02) and the estimated blood losswas 226 mL for ICAN versus 483 mL for ECAN (p = 0.004).Intraoperative and postoperative complications were low usingeither technique and differences were not statistically significant.Overall, the preoperative to postoperative change in the percentageof panel reactive antibody was +2.1% for ICAN versus +1.2% forECAN (NS) at 3 to 12 months postoperatively, respectively (NS).The percentage of patients relisted was 33.3% versus 54.3% (NS),and the percentage of patients re-transplanted once relisted wasalso very similar: 63.2% for ECAN versus 66.7% for ICAN (NS),after a mean follow-up of 4.5 and 8.4 years, respectively.Conclusions: ICAN can be performed with shorter operative timesand less blood loss versus the extracapsular approach. As well, thisoperative approach does not appear to affect allosensitization andthe ability to re-transplant 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".