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Record W4206921659 · doi:10.1101/2022.01.18.22269447

Life-long effects of malnutrition using semi-quantitative EEG analysis

2022· preprint· en· W4206921659 on OpenAlexaff
Fuleah A. Razzaq, Ana Calzada Reyes, Qin Tang, Yanbo Guo, Yujie Liu, Lídice Galán-García, Anne Gallagher, Trinidad Virues‐Alba, Carlos Suárez-Murias, Arielle Rabinowitz A., Ileana Miranda, Vivian Bernardo Lagomasino, Janina R. Galler, María L. Bringas-Vega, Pedro A. Valdés‐Sosa

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsElectroencephalographyPsychologyAudiologyMalnutritionNeurophysiologyMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

1 ABSTRACT The non-linear spatiotemporal features in the continuing EEG recordings could be helpful to infer the physio and pathological significance of early insults on the brain, such as early malnutrition and their long-term effects. A unique opportunity is opened with the Barbados Nutrition Study (BNS) dedicated to studying Protein-Energy Malnutrition (PEM) with two groups, children suffering an early PEM episode and their controls. We evaluated the resting-state EEG (N=108, PEM=46) in 1978, and we repeated the EEG (N=97, PEM=46) in 2018. We did a qualitative analysis of the EEG using a semi-quantitative scale (Grand Total EEG (GTE)) and an item response theory (IRT) approach to estimate a latent variable that is able to explain the subjacent neurophysiological status (NPS). Finally, we applied a mixed-effects model with a sensitivity index for ignorability to test differences between the controls and PEM groups while accounting for the missing data mechanisms ( nlme (Pinheiro J. 2020) and the ISNI package in R(Xie et al., 2018). The fixed effects were group, age, gender, and socioeconomic status; the random effect was the variability inherent to each participant and evaluator. Results The simple visual inspection of the 1978 EEG recordings detected 39 participants with abnormalities (28 PEM and 11 Controls; p<0.05); in 2018, a total of 63 participants showed abnormalities in the EEG recordings (35 PEM and 28 Controls; p<0.01)). The polytomous IRT analysis revealed that all items had been loaded well onto the latent factor, and the highest value of the Focal abnormality reached 0.97. The fixed effect of the groups (PEM vs. Control) was highly significant, with a p-value of 0 and the c index of 5.27 . Age was also significant with a p-value of 0.0093 and the c index of 14.793 , whereas Gender and SES were not significant. The contrasts at the two different time points (childhood (1978) mean age= 8.45, adulthood (2018) mean age=48.30) also showed highly significant differences between groups with a p-value of 0 . Conclusions EEG abnormalities were seen in both PEM and control groups during the school years and later in middle adulthood, with a higher proportion of abnormalities in the previously malnourished BNS participants at both ages. The statistical significance of these differences was confirmed through a latent variable approach and a linear mixed-effect model, which discriminated successfully against the long-term effects of early malnutrition on the brain up to 50 years after the onset of malnutrition in the first year of life.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.322
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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