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Record W2029332991 · doi:10.1300/j013v39n02_04

After the Fall: Women's Views of Fractures in Relation to Bone Health at Midlife

2004· article· en· W2029332991 on OpenAlexaff
Lynn M. Meadows, Linda A. Mrkonjic, Kimberly M. A. Petersen, Laura Lagendyk

Bibliographic record

VenueWomen & Health · 2004
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineOsteoporosisDiseaseBone healthPublic healthOccupational safety and healthGerontologyBone mineralNursing

Abstract

fetched live from OpenAlex

Past research has established the link between low energy fractures and the risk for future fractures. These fractures are potential markers for investigation of bone health, and may be precursors for osteoporosis. In spite of its significant public health burden, including burden of illness and economic costs, many individuals are not aware of the risk factors for and consequences of osteoporosis. This is a study of women aged 40 and older who experienced low energy fractures (e.g., from non-trauma sources and falls from no higher than standing height). We gathered data, using focus group interviews, about their experiences and understanding of the fractures in relation to bone health. Women often attributed the fractures to particular situations and external events (e.g., slipping on ice, tripping on uneven ground), and viewed the fractures as accidents. Women often felt that others are at risk for poor bone health, but believed that they themselves are different from those really at risk. Although the fractures are potential triggers for preventive efforts, few women connected their fracture to future risk. What is perceived by women (and others) as random and an accident is often a predictable event if underlying risk factors are identified. Only when there is more awareness of poor bone health as a disease process and fractures as markers for bone fragility will women, men and health care providers take action to prevent future fractures and established bone disease.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.366
Teacher spread0.339 · 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 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

Citations34
Published2004
Admission routes1
Has abstractyes

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