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Record W3000876908 · doi:10.15586/jptcp.v26i3.629

THE IMPORTANCE OF RESTORING BODY FAT MASS IN THE TREATMENT OF ANOREXIA NERVOSA: AN EXPERT COMMENTARY

2019· editorial· en· W3000876908 on OpenAlexvenueno aff
Agnes Ayton

Bibliographic record

VenueJournal of Population Therapeutics and Clinical Pharmacology · 2019
Typeeditorial
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsAnorexia nervosaMenstruationMedicineAnorexiaEating disordersEndocrine systemPhysiologyPsychologyFertilityHormoneEndocrinologyPsychiatryInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Anorexia nervosa is a severe mental disorder that is characterised by dietary restriction, low weight and widespread endocrine abnormalities. Whilst the importance of weight restoration has been recognised in recent guidelines, the significance of normalising body fat mass has received less attention. A recent systematic review and meta-analysis found that a minimum of 20.5% body fat mass is necessary for regular menses in women with anorexia nervosa of reproductive age. This has significant implications for both treatment and research. It is important to help the patient and carers understand that a certain level of body fat percentage is essential for optimal health, such as the return of menstruation. Further research is needed into how best to use this information to help motivation to change as part of treatment. The benefit of the return of menstruation goes beyond improved fertility: it signals the normalisation of sexual hormones, which have a widespread impact on the body and multiple pathways in the brain. Given the complex functions of adipocytes in various organs of the body, the metabolic effects of the normal body fat tissue should not be underestimated. Further research is needed to elucidate the mechanisms behind the link between minimum body fat mass, menstruation, bone and brain health in anorexia nervosa.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.485
Teacher spread0.402 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations10
Published2019
Admission routes1
Has abstractyes

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