Evidence map›Paper›PMID 41777105›Full record

ArticleThe British journal of nutrition2026

Developing and validating machine learning algorithms to predict various indices of diet quality among a socio-economically disadvantaged group.

Mélina Côté, Marianne Rochette, Catherine Laramée, Annie Lapointe, Sharon I Kirkpatrick, Simone Lemieux, Sophie Desroches, Ariane Bélanger-Gravel, Benoît Lamarche

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Mélina CôtéCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Marianne RochetteCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Catherine LaraméeCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Annie LapointeCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Sharon I KirkpatrickSchool of Public Health Sciences, University of Waterloo, Waterloo, ONN2L 3G1, Canada.ORCID https://orcid.org/0000-0001-9896-5975
Simone LemieuxCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Sophie DesrochesCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Ariane Bélanger-GravelCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.
Benoît LamarcheCentre Nutrition, Santé et Société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QCG1V 0A6, Canada.ORCID https://orcid.org/0000-0002-4443-5378

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health research faces challenges in recruiting socio-economically disadvantaged groups. This study evaluated whether machine learning (ML) algorithms developed using data from a general population could predict indices of diet quality among a socio-economically disadvantaged group. Data from 5367 adults (77·5 % females) in the NutriQuébec project and on 122 variables potentially associated with dietary intakes were used. Dietary intakes were measured using a web-based 24-h recall. Participants were categorised by fifths of a deprivation score based on income, education and material and social deprivation. Participants in the first four fifths formed the general NutriQuébec sample (

Indexed as

DietDiet, HealthyMachine LearningVulnerable PopulationsAdultAlgorithmsCanadaClassification AlgorithmsFemaleFruitHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRandom ForestDiet qualityHigh deprivationLow socio-economic statusMachine learningNutriQuébecPublic healthRandom forest

Identifiers

PMID41777105
PMCPMC13423524

What Socratic holds

Textmetadata
LicenceCC BY-NC-SA
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 Socratic graph.