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Record W1998263569 · doi:10.1002/eat.22102

Bone health in adolescent females with anorexia nervosa: What is a clinician to do?

2013· article· en· W1998263569 on OpenAlexaff
Debra K. Katzman, Madhusmita Misra

Bibliographic record

VenueInternational Journal of Eating Disorders · 2013
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsAnorexia nervosaMedroxyprogesterone acetateMedroxyprogesteroneMedicineBone mineralGirlWeight gainPediatricsAnorexiaInternal medicineEndocrinologyGynecologyEating disordersOsteoporosisHormonePsychologyBody weightPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

The objective of this case report is to present a pharmacologic strategy for treatment of adolescents with anorexia nervosa (AN) and low bone mineral density (BMD). We present a 17.5-year-old girl with a 3-year history of AN and longstanding inability to optimize nutrition and gain weight, and a decrease over time in her already low BMD. A year after treatment with the 17-β estradiol patch (100 mcg twice weekly) with cyclic oral progesterone (2.5 mg medroxyprogesterone acetate daily for days 1-10 of every month), her spine and hip BMD Z-scores improved, and a further decrease was prevented. This novel treatment is a consideration for girls with AN at greatest risk for low BMD. Adolescents with AN are at risk for low BMD, and the most effective treatment is weight and menses restoration, which can be difficult to attain and to sustain. Recent studies have shown promising results with pharmacological therapy for low BMD in AN. This article discusses current concepts related to bone loss in AN, and new pharmacologic considerations for adolescents at greatest risk for low BMD.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.373
Teacher spread0.340 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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