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Record W4408966457 · doi:10.1177/20543581251324610

Testicular Pain After Living Kidney Donation: Results From a Multicenter Cohort Study

2025· article· en· W4408966457 on OpenAlexafffundabout
Amit X. Garg, Liane S. Feldman, Jessica M. Sontrop, Meaghan S. Cuerden, Jennifer Arnold, Neil Boudville, Martin Karpinski, Scott Klarenbach, Greg Knoll, Charmaine E. Lok, Eric McArthur, Matthew S. Miller, Mauricio Monroy‐Cuadros, Kyla L. Naylor, G. V. Ramesh Prasad, Leroy Storsley, Christopher Nguan

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of British ColumbiaSt. Michael's HospitalUniversity of CalgaryOttawa HospitalUniversity Health NetworkMcGill UniversityUniversity of OttawaUniversity of AlbertaSt. Joseph’s Healthcare HamiltonVictoria HospitalUniversity of ManitobaImpactMcMaster UniversityLondon Health Sciences CentreUniversity of TorontoWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineKidney donationMulticenter studyCohortCohort studyNephrologyDonationKidney transplantationInternal medicineKidney

Abstract

fetched live from OpenAlex

Background: Some men who donate a kidney have reported testicular pain after donation; however, attribution to donation is not clear as no prior studies included a comparison group of nondonors. Objective: To examine the proportion of male donors who reported testicular pain in the years after nephrectomy compared to male nondonors with similar baseline health characteristics. Design Participants and Setting: We enrolled 1042 living kidney donors (351 male) before nephrectomy from 17 transplant centers (12 in Canada and 5 in Australia) from 2004 to 2014. A concurrent sample of 396 nondonors (126 male) was enrolled. Follow-up occurred until November 2021. Measurements: Donors and nondonors completed the same schedule of measurements at baseline (before nephrectomy) and follow-up. During follow-up, participants completed a questionnaire asking whether they had experienced new pain in their eyes, hands, or testicles; those who experienced pain were asked to indicate on which side of the body the pain occurred (left or right). The pain questionnaire was completed by 290 of 351 male donors (83%) and 97 of 126 male nondonors (77%) a median of 3 years after baseline (interquartile range = 2-6). Methods: Inverse probability of treatment weighting on a propensity score was used to balance donors and nondonors on baseline characteristics. After weighting, the nondonor sample increased to a pseudo sample of 295, and most baseline characteristics were similar between donors and nondonors. Results: At baseline, donors (n = 290) were a mean age of 49 years; 83% were employed, and 80% were married; 246 (84.8%) underwent laparoscopic surgery and 44 (15.2%) open surgery; 253 (87.2%) had a left-sided nephrectomy and 37 (12.8%) a right-sided nephrectomy. In the weighted analysis, the risk of testicular pain was significantly greater among donors than nondonors: 51/290 (17.6%) vs 7/295 (2.3%); weighted risk ratio, 7.8 (95% confidence interval [CI] = 2.7 to 22.8). Donors and nondonors did not differ statistically in terms of self-reported eye pain or hand pain. Among donors, the occurrence of testicular pain was most often unilateral (92.2%) and on the same side as the nephrectomy (90.2%). Testicular pain occurred more often in donors who had laparoscopic vs open surgery: 48/246 (19.5%) vs 3/44 (6.8%) but was similar in those who had a left-sided vs right-sided nephrectomy: 44/253 (17.4%) vs 7/37 (18.9%). Limitations: Participants recalled their symptoms several years after baseline, and we did not assess the timing, severity, or duration of pain or any treatments received for the pain. Conclusion: Unilateral testicular pain on the same side of a nephrectomy is a potential complication of living kidney donation that warrants further investigation.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.264
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2025
Admission routes3
Has abstractyes

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