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Record W4417304395 · doi:10.1186/s40337-025-01490-w

Exploring markers of feasibility for a pragmatic study of biomarkers in adolescents with eating disorders: steps towards a precision psychiatry approach

2025· article· en· W4417304395 on OpenAlexaff
Mark L. Norris, Krista A. Power, Wendy Spettigue, Niana Lavallée, Madeline J. Gertler, D. Livingston, Alexane Rodrigue, Janessa Porter, Lori Pope, Megan Harrison, Nuray Kanbur, Gary S. Goldfield, Leanna Isserlin, Amy Robinson, Nicole Obeid

Bibliographic record

VenueJournal of Eating Disorders · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsPrecision medicineSleep (system call)MEDLINEActigraphy

Abstract

fetched live from OpenAlex

To examine markers of feasibility for a pragmatic interdisciplinary multi-axial study of biomarkers in adolescents with eating disorders (EDs). The study included the collection of medical and clinical variables, psychometric measures, dietary logs, sensory and sleep assessments, and biological samples (i.e., blood and stool collection) for biomarker analyses. Adolescents between the ages of 11 to 17 diagnosed with a restrictive ED, along with control participants were enrolled between November 2021 to July 2024. Participants with EDs underwent concurrent treatment while enrolled in the study. Time points for low-weight patients included baseline, 4, 12, and 26 weeks, depending on the biological marker. Control subjects and patients over 90% treatment goal weight were assessed once. Feasibility was evaluated using clinical participant recruitment efficiency and uptake, adherence to the study protocol, rates of study completion, and self-reported ratings of acceptability. In total, 100 participants with an ED, and 52 controls participated. We observed high rates of clinical participant enrolment, high adherence with most protocol collection procedures, modest dropout for longitudinal clinical participants (17% at 12 weeks), and positive feedback returned on surveys. We observed higher dropout at the 26-week timepoint (33%). Food log and sleep assessments were hindered by several contributing factors, resulting in completion rates of 31–70% and 40–87%. Overall, results suggest acceptable feasibility for most variables assessed. Protocols requiring participation beyond 12 weeks, and utilizing dietary logs and sleep assessments, should be powered accordingly to account for lower completion rates. Further studies are needed to determine methods to optimize dietary and sleep assessments. This study provides valuable insights that can inform future precision psychiatry ED research strategies. In this study, we report on the feasibility of collecting biomarker samples in adolescents with eating disorders (ED) to help inform a platform of precision psychiatry research. We included standardized questionnaires, dietary logs, sleep, sensory, and pharmacogenetic assessments, and collected blood and stool samples. Depending on the biomarker and grouping of participants, we collected data at 1, 3 or 4 timepoints. We evaluated feasibility using rates of recruitment, adherence to the protocol, timepoints and study completion, and overall acceptability. We recruited 100 participants with an ED and 52 controls. We observed high rates of clinical enrolment and adherence to most collection procedures, low rates of drop out over the first 12 weeks, and positive feedback returned on surveys. Dietary log completion and sleep assessments were the most challenging research tasks, with the highest rates of non-completion. Our results suggest that most participants followed longitudinally completed testing up to twelve weeks. Protocols requiring participation beyond this timeframe should account for substantial dropout. Further studies are required to investigate the optimal method to track nutritional intake and assess sleep characteristics for research purposes in adolescents with EDs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.341
Teacher spread0.290 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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