Improving Dietary Data Quality in Nutrition Studies: Development and Validation of an Explainable, Reproducible, Open Machine Learning-Assisted Framework for Outlier Detection in 24-Hour Recalls.
- DOI
- 10.1093/aje/kwag204
- Published
- 2026 Aug 24
- Container
- American journal of epidemiology
- Publisher
- Not recorded
- Open access
- no
Credibility signals
limited evidence Score 43/100 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.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- 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:10.1093/aje/kwag204,
title = {Improving Dietary Data Quality in Nutrition Studies: Development and Validation of an Explainable, Reproducible, Open Machine Learning-Assisted Framework for Outlier Detection in 24-Hour Recalls.},
author = {Massara P and Saab S and Asrar A and Omand J and Anderson LN and Keown-Stoneman C and Maguire JL and Bandsma RHJ and Birken CS and Comelli EM},
year = {2026},
journal = {American journal of epidemiology},
doi = {10.1093/aje/kwag204},
url = {https://doi.org/10.1093/aje/kwag204}
}RIS
TY - JOUR TI - Improving Dietary Data Quality in Nutrition Studies: Development and Validation of an Explainable, Reproducible, Open Machine Learning-Assisted Framework for Outlier Detection in 24-Hour Recalls. AU - Massara P AU - Saab S AU - Asrar A AU - Omand J AU - Anderson LN AU - Keown-Stoneman C AU - Maguire JL AU - Bandsma RHJ AU - Birken CS AU - Comelli EM PY - 2026 JO - American journal of epidemiology DO - 10.1093/aje/kwag204 UR - https://doi.org/10.1093/aje/kwag204 ER -
APA
P, M., S, S., A, A., J, O., LN, A., C, K., JL, M., RHJ, B., CS, B., & EM, C. (2026). Improving Dietary Data Quality in Nutrition Studies: Development and Validation of an Explainable, Reproducible, Open Machine Learning-Assisted Framework for Outlier Detection in 24-Hour Recalls.. American journal of epidemiology. https://doi.org/10.1093/aje/kwag204
Source records
- pubmed · retrieved 2026-09-26T04:13:41.139Z
- europe-pmc · retrieved 2026-09-26T04:13:41.159Z