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Record W4301135320 · doi:10.1017/cbo9781139150040

Medical Management of Eating Disorders

2010· book· en· W4301135320 on OpenAlexaff
C. Laird Birmingham, Janet Treasure

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEating disordersAnorexia nervosaHealth professionalsAffect (linguistics)MedicinePsychiatryPsychologyHealth careNursingCommunication

Abstract

fetched live from OpenAlex

Eating disorders are known to affect between 1 and 4% of all women, and a smaller proportion of men. This text is designed to provide all health professionals with the practical information they need to treat patients with anorexia nervosa and related eating disorders. A user-friendly structure allows the reader to access information on the basis of physical complaint. The book is divided into five sections, each consisting of a case, discussion of the topic and a summary of key points. For the second edition, sections have been added on Munchausen's syndrome, the medical risk of death, ruminating, Superior Mesenteric Artery Syndrome, shoplifting, substance use, and patient self-help. The text is supplemented with diagnostic color photographs of important physical manifestations of eating disorders. The text is suitable for all health care professionals involved in eating disorder management, with special information provided for general practitioners, nursing staff, family carers and nutritionists.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.011

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 head0.008
GPT teacher head0.205
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2010
Admission routes1
Has abstractyes

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