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Breast cancer and osteoporosis

2013· review· en· W2121530102 on OpenAlexafffund
Angela M. Cheung, Ruth Heisey, Jeevitha Srighanthan

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2013
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicineBreast cancerAromataseOsteoporosisAromatase inhibitorBone mineralInternal medicineOncologyCancerBisphosphonateAdverse effectGynecology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Over the past few years, a number of studies have examined the relationship between breast cancer and osteoporosis, the effect of breast cancer treatment on bone health, and the effect of osteoporosis therapies on aromatase inhibitor-induced bone loss and breast cancer recurrence. New guidelines have been released on the prevention of osteoporotic fractures in women with breast cancer who are on aromatase inhibitors for adjuvant therapy. RECENT FINDINGS: Despite common factors linking high bone mineral density and increased risk of breast cancer, women with breast cancer are not protected from osteoporosis or osteoporotic fractures. Recent data suggest that aromatase inhibitors have a detrimental effect on bone mineral density and can increase the risk of fractures. Bisphosphonate therapy not only preserves aromatase inhibitor-induced bone loss, but may also improve disease-free survival and decrease risk of death in select women with breast cancer (i.e., postmenopausal women). SUMMARY: Osteoporosis and breast cancer are common in women, especially in postmenopausal women. Current guidelines suggest that we need to pay special attention to those on aromatase inhibitors to prevent adverse bone outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.068
GPT teacher head0.382
Teacher spread0.315 · 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 designNot applicable
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

Citations22
Published2013
Admission routes2
Has abstractyes

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