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Record W2048058843 · doi:10.1002/eat.1094

The interrater reliability of physical signs in patients with eating disorders

2001· article· en· W2048058843 on OpenAlexaff
Ingrid Tyler, C. Laird Birmingham

Bibliographic record

VenueInternational Journal of Eating Disorders · 2001
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKappaInter-rater reliabilityBulimia nervosaEating disordersAnorexia nervosaCohen's kappaPsychologyPsychiatryMedicineDevelopmental psychologyStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the interrater reliability of five common signs of eating disorders. METHODS: Eating disorder patients with anorexia nervosa, bulimia nervosa, and eating disorders not otherwise specified (ED-NOS), at various stages of recovery, were evaluated for the presence or absence of lanugo hair, acrocyanosis, parotid hypertrophy, hypercarotinemia, and Russell's sign. Patients were examined by two physicians with similar experience and training. Results are analyzed for reliability using the kappa statistic. RESULTS: Kappa scores were as follows, indicating marginal reproducibility of results: lanugo hair (kappa = 0.606), acrocyanosis (kappa = 0.014), parotid hypertrophy (kappa = 0.266), hypercarotinemia (kappa = 0.101) , and Russell's sign (kappa = 0.140). CONCLUSION: The interrater reliability for individual items ranged from poor to moderate. Overall, there is marginal interrater reliability for the five common signs of eating disorders assessed.

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)0.020
metaresearch head score (Gemma)0.056
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 score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.009
GPT teacher head0.297
Teacher spread0.289 · 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

Citations41
Published2001
Admission routes1
Has abstractyes

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