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Record W4409786710 · doi:10.1249/mss.0000000000003742

Development and Validation of a Risk Assessment Tool for Energy Deficiency in Young Active Females: The Female Energy Deficiency Questionnaire (FED-Q)

2025· article· en· W4409786710 on OpenAlexaff
Ana Carla Chierighini Salamunes, Nancy I. Williams, Marion P. Olmsted, Kristen J. Koltun, Prabhani Kuruppumullage Don, Mary Jane De Souza

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBody mass indexMedicineMenarcheLogistic regressionDisordered eatingMenstrual cycleEating disordersInternal medicinePhysical therapyDemographyEndocrinologyClinical psychologyHormone

Abstract

fetched live from OpenAlex

PURPOSE: We aimed to develop and validate a risk assessment tool for energy deficiency in young exercising women using disordered eating subscales and self-reported health-related information. METHODS: We retrospectively analyzed seven studies in competitive and recreationally active women ( n = 202; age, 21.7 ± 0.3 yr; body mass index, 21.21 ± 0.14 kg·m -2 (mean ± SEM)). Participants completed the Health, Exercise, and Nutrition Survey, the Three-Factor Eating Questionnaire, and the Eating Disorder Inventory-3. Energy deficiency was defined as fasting serum total triiodothyronine (TT 3 ) <73.2 ng·dL -1 . A cutoff of TT 3 <80 ng·dL -1 was also tested. Potential predictors of energy deficiency were as follows: age of menarche, gynecological age, disordered eating, menstrual status, and bone health items (Health, Exercise and Nutrition Survey); dietary cognitive restraint (Three-Factor Eating Questionnaire); and Perfectionism, Body Dissatisfaction, and Drive for Thinness (Eating Disorder Inventory-3). A model set ( n = 152; 21.8 ± 0.3 yr, 21.23 ± 0.16 kg·m -2 ) was used to select predictors, identify interaction terms, and fit 500 random iterations of stepwise logistic regression. Predictors included in ≥475 models were used in a final model and tested on a validation set ( n = 50; 21.6 ± 0.4 yr, 21.15 ± 0.3 kg·m -2 ). RESULTS: The final model included body mass index, number of menstrual cycles in the last 6 months, dietary cognitive restraint, and body dissatisfaction index. The Female Energy Deficiency Questionnaire coefficient detected TT 3 <73.2 ng·dL -1 with 84.2% sensitivity, 80.6% specificity, and 82% accuracy, and TT 3 <80 ng·dL -1 with 85% sensitivity, 83.3% specificity, and 84% accuracy. CONCLUSIONS: At present, the Female Energy Deficiency Questionnaire is the only questionnaire that is specifically designed as an indicator of energy deficiency in female athletes across a variety of sports.

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)0.012
metaresearch head score (Gemma)0.018
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.328
Teacher spread0.311 · 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
Published2025
Admission routes1
Has abstractyes

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