Evidence map›Paper›PMID 42884684›Full record

ArticleNutrients2026

Internal Consistency Does Not Validate an LLM-Assisted Nutrition Pipeline: A Reference-Standard-Free Forensic Reliability Audit of 13,073 Free-Text Meal Records.

Jhony Fernando López Torres, Juan Gabriel Castañeda, Edwin Rivas

Abstract readValidation Study
In one paragraph

Article in Nutrients, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jhony Fernando López TorresNeptura, Libano 731040, Tolima, Colombia.ORCID 0009-0009-7202-2915
Juan Gabriel CastañedaFacultad de Ciencias Humanas, Corporación Universitaria Minuto de Dios-UNIMINUTO, Bogotá D.C. 111021, Colombia.ORCID 0000-0001-7632-7526
Edwin RivasFacultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 111711, Colombia.ORCID 0000-0003-2372-8056

Funding

Ministerio de Ciencia, Tecnología e Innovación BIOMARCADORES-111598
6 · The paper itself

Abstract

BACKGROUND/

objectivesLarge language model (LLM) pipelines increasingly convert free-text meal descriptions into nutrient values, yet reference-standard validation is rarely feasible in production. We asked whether the controls such pipelines conventionally pass suffice and built a reference-standard-free battery any pipeline can run on its output.

methodsWe audited 13,073 meals logged by 513 adults through a WhatsApp conversational agent in rural Colombia (9 February 2026 to 6 August 2026). The pipeline is a two-stage cascade of commercial LLMs from different providers: a vision model renders photographs as text, while a text model maps these to nutrients. Six pre-specified tests were applied, including test-retest ICC(1,1) over recurring strings, stratified by modality because 48.3% of records carried a photograph.

resultsThe pipeline passed every conventional control (Atwater-reconstructed energy r = 0.9951; median relative error 0.0000) but failed reproducibility. Across 191 recurring strings (944 meals), energy ICC point estimates were 0.7061 (95% CI 0.5419-0.8217) text only and 0.5258 (0.3778-0.6513) with photographs; none reached 0.75. Modality is a design control, not an established effect, and the coefficient depends on which inputs recur, so it is not intrinsic. Under the strictest identity available-byte-identical text, no photograph-ICC was 0.6944. Intermediate-text inspection exposed a failure invisible at the output: 1.44% of photograph-accompanied records carried a vision-stage artefact string versus 0.01% of text-only records (ICC 0.0911).

conclusionsInternal consistency does not validate an LLM-assisted nutrition pipeline. This design measures instability under recurring inputs without isolating its cause. Prospective test-retest reproducibility is a cheap, necessary acceptance criterion, and in a cascade, the intermediate representation must also be inspected.

Indexed as

Diet RecordsLarge Language ModelsMealsNutritive ValueAdultColombiaFemaleHumansMaleReference StandardsReproducibility of Resultsartificial intelligencedata accuracydiet recordslarge language modelsnutrition assessmentreproducibility of results

Identifiers

PMID42884684
PMCPMC13667244

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Registered trials

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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.