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Bone Changes and Fracture Related to Menstrual Cycles and Ovulation

2010· review· en· W2061614285 on OpenAlexaff
Shirin Kalyan, Jerilynn C. Prior

Bibliographic record

VenueCritical Reviews in Eukaryotic Gene Expression · 2010
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsOsteoporosisBone remodelingMenstrual cycleOvulationMedicinePhysiologyBioinformaticsEndocrinologyBiologyHormone

Abstract

fetched live from OpenAlex

Women's menstrual cycles and bone remodeling are linked in part by their co-dependency on the stress- and resource-associated variables that govern both of their cyclical natures. Therefore, it is not surprising that evolution has resulted in the same signaling molecules and pathways that regulate normal ovarian function to be involved in bone remodeling and turnover. This review will first provide an overview of the normal menstrual cycle, its modification by age and ovulatory disturbances, and how it parallels bone remodeling. Epidemiological and clinical evidence will be presented that link bone remodeling, strength, and fractures with women's history of reproductive and menstrual cycle characteristics. This combined evidence will then be presented alongside a synthesis of current concepts derived from basic science investigations focused on understanding the molecular mechanisms underlying the influence of ovarian factors on bone physiology. Osteoporosis is a significant source of morbidity for older women. The data presented in this review suggest that a woman's reproductive cycle and ovulatory characteristics foreshadow the future health of her bones. More importantly, identifying the key mechanisms underlying reproductive and bone health would not only provide essential preventative strategies, but may also uncover attractive targets for the treatment of osteoporosis.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.069
GPT teacher head0.438
Teacher spread0.369 · 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 designOther design
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

Citations26
Published2010
Admission routes1
Has abstractyes

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