Use of urinary markers in cancer setting: A literature review
Bibliographic record
Abstract
INTRODUCTION: In bone metastases, the disruption of normal bone processes results in increased resorption and formation rates, which can often be quantitatively measured by biomarkers in the urine and blood. The purpose of this review is to summarize relevant studies of urinary markers used as a diagnostic and/or prognostic tool, as well as its potential and advances in directing therapy. METHODS: A literature search was conducted using Ovid MEDLINE (1950 to July 2014), EMBASE (1950 to 2014 week 30) and Cochrane Central Register of Controlled Trials (3rd Quarter 2014) to identify studies that detailed the use of urinary markers in the cancer setting, specifically involving markers for bone metastases. Search terms included "urinary markers", "cancer", and "bone metastases". RESULTS: A total of 35 articles, with 24 original studies, were identified. In general, urinary markers can be used to detect early signs of bone metastases prior to skeletal imaging, but still must be used in conjunction with imaging to avoid false positive results. The use of urinary markers, such as N-telopeptide, as a prognostic tool remains controversial, but can provide information on the relative risk of skeletal related events (SREs), disease progression, as well as death. Finally, while urinary markers have shown to be potentially useful in confirming the efficacy of bone metastases treatments, exploring the appropriate dosages for treatment, and directing therapy, it is still unclear to what extent urinary markers should be reduced by. CONCLUSION: The potential use of urinary markers in the management of bone metastases is promising as it can allow for earlier and more convenient detection of bone metastases in comparison to other techniques. However, additional studies involving prospective clinical trials are suggested to further examine the potential of urinary markers in developing appropriate treatment strategies and endpoints, especially in developing a clearer protocol on the extent urinary markers should be reduced by to correlate with achievement of clinical benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".