---
res:
  bibo_abstract:
  - "<jats:title>ABSTRACT</jats:title>\r\n                  <jats:sec>\r\n                    <jats:title>Objective</jats:title>\r\n
    \                   <jats:p>Epidemiological evidence on muscle dysmorphia (MD)
    remains limited, and self‐report algorithm‐defined estimates may depend on sampling
    and case definitions. We examined how algorithm‐defined MD estimates and exploratory
    correlates varied across weighting and case‐definition scenarios in a gender‐balanced
    German online sample.</jats:p>\r\n                  </jats:sec>\r\n                  <jats:sec>\r\n
    \                   <jats:title>Method</jats:title>\r\n                    <jats:p>In
    this cross‐sectional web‐based study, 1468 adults from Germany completed self‐report
    measures: 739 (50.3%) men, 706 (48.1%) women, 21 (1.4%) nonbinary/diverse participants,
    and 2 (0.1%) who preferred not to disclose their gender. Algorithm‐defined MD
    was estimated using a self‐report algorithm derived from prior epidemiological
    work. Estimates were compared across four scenarios combining unweighted versus
    age‐ and gender‐weighted analyses with global versus gender‐specific criterion
    A cutoffs. Logistic regression models examined correlates across weighted and
    unweighted analytic specifications.</jats:p>\r\n                  </jats:sec>\r\n
    \                 <jats:sec>\r\n                    <jats:title>Results</jats:title>\r\n
    \                   <jats:p>Algorithm‐defined estimates varied substantially across
    operationalizations. The unweighted global algorithm yielded an estimate of 6.2%
    overall and 11.0% in men. The weighted gender‐specific scenario yielded 2.6% overall,
    3.5% in men, and 1.7% in women. Across regression specifications, lower BMI and
    higher identity disturbance were the most consistent correlates of algorithm‐defined
    MD. Female gender showed lower odds in pooled models, particularly under the global
    cutoff, but this finding should be interpreted cautiously.</jats:p>\r\n                  </jats:sec>\r\n
    \                 <jats:sec>\r\n                    <jats:title>Discussion</jats:title>\r\n
    \                   <jats:p>Self‐report algorithm‐defined MD may affect a meaningful
    minority of adults, but estimates depend strongly on weighting strategy and case
    definition. These findings highlight the need for transparent reporting of algorithmic
    operationalizations.</jats:p>\r\n                  </jats:sec>@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christopher
      foaf_name: Zaiser, Christopher
      foaf_surname: Zaiser
  - foaf_Person:
      foaf_givenName: Nora M.
      foaf_name: Laskowski, Nora M.
      foaf_surname: Laskowski
  - foaf_Person:
      foaf_givenName: Georg
      foaf_name: Halbeisen, Georg
      foaf_surname: Halbeisen
      foaf_workInfoHomepage: http://www.librecat.org/personId=85780
    orcid: 0000-0002-9529-2215
  - foaf_Person:
      foaf_givenName: Marietta
      foaf_name: Lieb, Marietta
      foaf_surname: Lieb
  - foaf_Person:
      foaf_givenName: Georgios
      foaf_name: Paslakis, Georgios
      foaf_surname: Paslakis
  bibo_doi: 10.1002/eat.70170
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0276-3478
  - http://id.crossref.org/issn/1098-108X
  dct_language: eng
  dct_publisher: Wiley@
  dct_title: Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition
    Strategies in a Gender‐Balanced Adult Online Sample@
...