Machine Learning Approaches to Predict 24-Hour Urine Collection Results Based on Self-Reported Beverage Intake.
- DOI
- 10.1053/j.jrn.2026.03.005
- Published
- 2026 Sep
- Container
- Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1053/j.jrn.2026.03.005,
title = {Machine Learning Approaches to Predict 24-Hour Urine Collection Results Based on Self-Reported Beverage Intake.},
author = {Li S and Streeper N and Ram N and Zoellner J and Penniston K and Conroy DE},
year = {2026},
journal = {Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation},
doi = {10.1053/j.jrn.2026.03.005},
url = {https://doi.org/10.1053/j.jrn.2026.03.005}
}RIS
TY - JOUR TI - Machine Learning Approaches to Predict 24-Hour Urine Collection Results Based on Self-Reported Beverage Intake. AU - Li S AU - Streeper N AU - Ram N AU - Zoellner J AU - Penniston K AU - Conroy DE PY - 2026 JO - Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation DO - 10.1053/j.jrn.2026.03.005 UR - https://doi.org/10.1053/j.jrn.2026.03.005 ER -
APA
S, L., N, S., N, R., J, Z., K, P., & DE, C. (2026). Machine Learning Approaches to Predict 24-Hour Urine Collection Results Based on Self-Reported Beverage Intake.. Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation. https://doi.org/10.1053/j.jrn.2026.03.005
Source records
- pubmed · retrieved 2026-09-26T17:54:33.574Z