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Record W3038799604 · doi:10.21037/qims-2020-21

Radiographic osteoporotic vertebral fractures in elderly men: a brief review focusing on differences between the sexes

2020· review· en· W3038799604 on OpenAlexaff
Yi-Xiang Wang, Brian C. Lentle

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOsteoporosisMenopauseRadiographyPhysical therapyBone mineralEpidemiologyInternal medicinePediatricsSurgery

Abstract

fetched live from OpenAlex

Bone loss occurs in both sexes as a result of ageing but is exacerbated in women by the hormonal changes associated with menopause. Unlike in women, secondary osteoporosis occurs in almost half of men diagnosed with osteoporosis. Moreover, vertebral fractures (VFs) seen in elderly men may more likely be the result of high energy trauma. The osteoporotic vertebral fracture (OVF) radiograph diagnosis criteria for women may not be directly applicable for men. Particular attention should be paid to the mid-thoracic level where over-diagnosis commonly occurs. For wedge-shaped vertebral deformities (VDs) or VDs with anterior height reduction only, a diagnosis of OVF requires great caution, as they are poorly correlated to bone mineral density (BMD). For age-matched subjects, it is likely that elderly men's prevalent radiographic OVFs are approximately half of the elderly women's. This male-female ratio is very similar to other clinical fractures such as those occurring at the hip. Even so, the clinical relevance of OVF in elderly men may be less than that of elderly women. On the other hand, for elderly men with hip BMD-based osteoporosis, the OVF risk is as high as that of osteoporotic women. Elderly Chinese men have a lower OVF prevalence than age-matched Caucasian men.

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)0.004
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 score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.125
GPT teacher head0.429
Teacher spread0.304 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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