Evidence map›Paper›PMID 40891338›Full record

ArticleClinical transplantation2025

Accuracy, Clarity, and Comprehensiveness of ChatGPT Outputs for Commonly Asked Questions About Living Kidney Donation.

Ria Singla, Sumiya Lodhi, Taddele Kibret, Januvi Jegatheswaran, Tamara Glavinovic, David Massicotte-Azarniouch, Jolanta Karpinski, Rinu Powell, Kevin Burns, Manish M Sood and 1 more

Abstract read
In one paragraph

Article in Clinical transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ria SinglaFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Sumiya LodhiFaculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Taddele KibretSchool of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Januvi JegatheswaranDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Tamara GlavinovicDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
David Massicotte-AzarniouchDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Jolanta KarpinskiDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Rinu PowellDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Kevin BurnsDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Manish M SoodDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.
Ann BugejaDivision of Nephrology, Department of Medicine, The Ottawa Hospital and Kidney Research Centre, Ottawa, Canada.ORCID https://orcid.org/0000-0002-4106-0451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe effectiveness of ChatGPT responses to common living kidney donation (LKD) queries remains unclear.

methodsWe surveyed nephrologists and living kidney donors/candidates to evaluate ChatGPT-3.5's accuracy, comprehensiveness, and clarity in answering common donation questions in English and French. Ratings used a 5-point Likert scale, with percentage agreement and modified Fleiss' Kappa measuring inter-rater consistency.

resultsThe evaluation of ChatGPT-3.5's responses varied between nephrologists and kidney donors/candidates. Nephrologists showed moderate percentage agreement for English responses (50%-59%) and poor agreement for French responses (9%-45%). Kidney donors/candidates exhibited high agreement for English (90%-100%) but low for French (0%-77%). Inter-rater agreement among nephrologists was moderate for both English (Kappa 0.74, 95% CI: 0.67, 0.79, p < 0.0001) and French (Kappa 0.70, 95% CI: 0.64, 0.77, p < 0.0001). In contrast, inter-rater agreement was poor among donors/candidates for both English (Kappa -0.10, 95% CI: -0.14, -0.07, p = 0.99) and French (Kappa -0.03, 95% CI: -0.07, 0, p = 0.81).

conclusionChatGPT 3.5's responses to common LKD queries demonstrated limited agreement among nephrologists and kidney donors/donor candidates, highlighting its lack of reliability as a supplement to existing educational materials for living kidney donor programs in English and French.

Indexed as

Kidney TransplantationLiving DonorsNephrectomyNephrologistsTissue and Organ ProcurementAdultFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPrognosisSurveys and Questionnaires

Identifiers

PMID40891338
PMCPMC12402969

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.