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Record W2316264820 · doi:10.1097/spc.0b013e328356da87

Preventing bone complications in prostate cancer

2012· review· en· W2316264820 on OpenAlexaff
Mohamed Bishr, Fred Saad

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2012
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineProstate cancerDenosumabZoledronic acidOsteoclastAndrogen deprivation therapyOncologySpinal cord compressionOsteoporosisInternal medicineCancerTargeted therapyQuality of life (healthcare)Spinal cord

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Bone metastases alone or in combination with androgen deprivation therapy-related bone loss places prostate cancer patients at greater risk for skeletal morbidities, including pain, pathologic fracture and spinal cord compression. These events significantly impair the patient's quality of life and place a significant burden on health-care resources. RECENT FINDINGS: This review focuses on the management options for reducing skeletal morbidity in patients with prostate cancer, including life-style modifications, food supplementation, osteoclast-targeted therapy and selective estrogen-receptor modulators. SUMMARY: The use of osteoclast-targeted therapy (denosumab and zoledronic acid) is supported by the strongest evidence and has been US Food and Drug Administration-approved for the treatment of patients with PCa at high risk of osteoporotic fractures and for the reduction of the risk of skeletal-related events in patients with castration-resistant prostate cancer. Ongoing trials are studying the potential role of osteoclast-targeted therapy in other settings throughout the course of the 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)0.000
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 score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.266
GPT teacher head0.507
Teacher spread0.241 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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