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Record W4380575691 · doi:10.46747/cfp.6906387

Approach to anorexia nervosa and atypical anorexia nervosa in adolescents

2023· review· en· W4380575691 on OpenAlexaffvenue
Rabea Parpia, Wendy Spettigue, Mark L. Norris

Bibliographic record

VenueCanadian Family Physician · 2023
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsChildren's Hospital of Eastern OntarioCollege of Family Physicians of Canada
Fundersnot available
KeywordsAnorexia nervosaEating disordersPsychiatryMedicineAnorexiaIntervention (counseling)Family therapyPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To address screening, diagnosis, and treatment of adolescents with anorexia nervosa and atypical anorexia nervosa in primary care. SOURCES OF INFORMATION: . Applicable articles were reviewed, with key recommendations summarized. Most evidence is level I. MAIN MESSAGE: Recent studies suggest that the global COVID-19 pandemic contributed to an increase in the incidence of eating disorders, particularly among teenagers. This has resulted in increasing responsibility for primary care providers regarding the assessment, diagnosis, and management of these disorders. Moreover, primary care providers are in ideal positions to identify adolescents at risk of eating disorders. Early intervention is of utmost importance for avoiding long-term health consequences. High rates of atypical anorexia nervosa indicate a need for providers to have awareness of weight biases and stigmas. Treatment primarily involves a combination of renourishment and psychotherapy, generally through family-based therapy, with pharmacotherapy playing a lesser role. CONCLUSION: Anorexia nervosa and atypical anorexia nervosa are serious, potentially life-threatening illnesses that are best addressed through early detection and treatment. Family physicians are in an optimal position to screen for, diagnose, and treat these illnesses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.073
GPT teacher head0.332
Teacher spread0.259 · 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 teacher head, not a consensus.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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