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Record W2121234560 · doi:10.1177/1534650106298917

The Girl Who Ate Her House—Pica as an Obsessive-Compulsive Disorder

2008· article· en· W2121234560 on OpenAlexaff
Yonas Baheretibeb, Samuel Law, Clare Pain

Bibliographic record

VenueClinical Case Studies · 2008
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPica (typography)PsychologyObsessive compulsiveDistressGirlEating disordersEtiologyPsychopathologyAnxietyPsychiatryPsychotherapistDevelopmental psychology

Abstract

fetched live from OpenAlex

This report concerns the interesting clinical phenomenology of a 17-year-old Ethiopian female student with a long-standing history of ingesting nonnutritive materials. She was initially non-selective, but later began more exclusively consuming mud obtained from a wall in front of her house. She suffered from a feeding and eating disorder known as pica. Currently, there is no clearly established etiology for pica. This patient's particular psychopathology—recurrent, unwanted, intrusive images and thoughts of the mud wall and of eating the mud; feelings of distress and anxiousness that were not relieved unless she consumed mud; and significant effects on her daily life from her uncontrollable need to return home to eat mud from her wall—suggests an ego-dystonic, obsessive thought-distress-consumption-relief pattern that is consistent with obsessive-compulsive disorder. This case may contribute to the etiological understanding that some forms of pica may be part of the obsessive-compulsive spectrum disorders.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.293
GPT teacher head0.577
Teacher spread0.284 · 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 designCase report
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

Citations5
Published2008
Admission routes1
Has abstractyes

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