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Record W1605287713 · doi:10.3747/co.2008.174

Cancer Treatment–Related Bone Loss: A Review and Synthesis of the Literature

2008· review· en· W1605287713 on OpenAlexaffvenue
M.N. Khan, Aliya Khan

Bibliographic record

VenueCurrent Oncology · 2008
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAndrogen deprivation therapyBone remodelingAromataseOsteoporosisProstate cancerInternal medicineBone mineralEndocrinologyOncologyBreast cancerBone Density Conservation AgentsCancerBioinformatics

Abstract

fetched live from OpenAlex

Cancer therapy can result in significant bone loss and increased risk of fragility fracture. Chemotherapy, aromatase inhibitors, and gonadotropin-releasing hormone analogues contribute to increases in the rate of bone remodelling and reduce bone mineral density. Patients with prostate cancer on androgen deprivation therapy experience an increase in the risk of fracture. New research has demonstrated the key role played by bisphosphonates in preventing declines in bone density and increases in bone remodelling. Novel antiresorptive agents targeting receptor activator of nuclear factor κB ligand have great potential in skeletal protection and prevention of bone loss related to cancer therapy. Early assessment of skeletal health, followed by initiation of calcium, vitamin D, and an exercise program are valuable in the prevention and treatment of osteoporosis. In addition, individuals at increased risk for fracture should be offered antiresorptive therapy. Early data have demonstrated that bisphosphonates are able to prevent the bone loss and increased bone remodelling associated with cancer therapy, including aromatase inhibition and androgen deprivation therapy. The present paper reviews the new research and advances in the management of bone loss associated with both cancer therapy and estrogen deficiency in the postmenopausal female.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.911
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.121
GPT teacher head0.466
Teacher spread0.345 · 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 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

Citations49
Published2008
Admission routes2
Has abstractyes

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