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Record W2017374472 · doi:10.1097/spc.0b013e32833ac6d6

Bone-marker levels in patients with prostate cancer: potential correlations with outcomes

2010· review· en· W2017374472 on OpenAlexaff
Fred Saad, Allan Lipton

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2010
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNovartis Pharmaceuticals CorporationAmgen
KeywordsMedicineProstate cancerZoledronic acidDenosumabBone metastasisBone remodelingN-terminal telopeptideCancerBone diseaseOncologyProstateInternal medicinePathologyOsteoporosisAlkaline phosphataseOsteocalcin

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The skeleton is typically the first site of metastasis in patients with prostate cancer, and bone metastases can result in severe bone pain and potentially debilitating fractures. Although bone scans are a reliable means of assessing osteoblastic lesions, tools for monitoring early changes in bone health are lacking. Biochemical markers of bone turnover might fulfill this unmet need. RECENT FINDINGS: Correlative studies have suggested that bone-marker levels may have utility in assessing disease progression and response to bone-directed therapy. Elevated levels of the markers, N-telopeptide of type I collagen and bone-specific alkaline phosphatase, are associated with higher rates of death and skeletal-related events in the bone metastasis setting. Marker levels also correlate with response to zoledronic acid treatment, and similar data with the investigational agent, denosumab, are emerging. SUMMARY: Changes in bone-marker levels reflect alterations in skeletal homeostasis and can provide important insights into bone disease progression and response to bone-directed therapy in patients with prostate cancer. More mature data from currently ongoing clinical trials will provide further insight on the utility of marker assessments as an adjunct to established monitoring methods in prostate cancer.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.081
GPT teacher head0.416
Teacher spread0.336 · 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 designObservational
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

Citations17
Published2010
Admission routes1
Has abstractyes

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