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Record W2974850256 · doi:10.1177/2054358119875459

Can Split Renal Volume Assessment by Computed Tomography Replace Nuclear Split Renal Function in Living Kidney Donor Evaluations? A Systematic Review and Meta-Analysis

2019· review· en· W2974850256 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2019
Typereview
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsLondon Health Sciences CentreVancouver General HospitalWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineMeta-analysisConfidence intervalRenal functionRandom effects modelKidneyNuclear medicineUrologyInternal medicine

Abstract

fetched live from OpenAlex

Background: As part of their living kidney donor assessment, all living donor candidates complete a computed tomography (CT) angiogram, but some also receive a nuclear renogram for split renal function (SRF%). Objective: We considered whether split renal volume (SRV%) assessed by CT can predict SRF%. Design: Systematic review and meta-analysis. Setting: Living donor candidates undergoing evaluation as potential living kidney donors. Patients: Living donor candidates who received both a nuclear renogram for split function and CT for SRV as part of their living donor work-up. Measurements: Split renal volume from CT scans and SRF from nuclear renography. Methods: We performed a systematic review and meta-analysis of the literature, abstracting data and digitizing plots where possible. We searched Medline, EMBASE, and the Cochrane Library. We added data from donor candidates assessed in London, Ontario from 2013 to 2016. We used fixed and random-effects models to pool Fisher’s z -transformed Pearson’s correlation coefficient ( r ). We conducted random-effects meta-regression on digitized and aggregate data. Studies were restricted to living kidney donors or living donor candidates. Results: After pooling 19 studies (n = 1479), we obtained a pooled correlation of r = 0.74 (95% confidence interval [CI] = 0.61-0.82). By linear regression using individual-level data, we observed a 0.76% (95% CI = 0.71-0.81) increase in SRF% for every 1% increase in SRV%. Split renal volume had a specificity of 88% for discriminating SRF at a threshold that could influence the decision of which kidney is to be removed (between-kidney difference ≥10%). Predonation SRV and SRF both moderately predicted kidney function 6 to 12 months after donation: r = 0.75 for SRV and r = 0.73 for SRF; Δ r = 0.05 (–0.02, 0.13). Limitations: Most studies were retrospective and measured SRV and SRF only on selected living donor candidates. Efficiency gains in removing the SRF from the evaluation will depend on the transplant program. Conclusion: Split renal volume has the potential to replace SRF for some candidates. However, it is uncertain whether it can do so reliably and routinely across different transplant centers. The impact on clinical decision-making needs to be assessed in well-designed prospective studies. Trial registration: The digitized data are registered with Mendeley Data (doi10.17632/dyn2bfgxxj.2).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.353
Teacher spread0.292 · 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