Circulating cancer stem cell markers in breast carcinomas: a systematic review protocol
Bibliographic record
Abstract
Abstract Background Breast cancer is one of the most common types of cancer in women worldwide. Recent studies have provided strong support for the cancer stem cell (CSC) hypothesis, which suggests that many cancers, including breast cancer, are driven by a subpopulation of cells that display stem cell-like properties. The hypothesis that a subpopulation of circulating tumor cells (CTCs) possesses many CSC-like hallmarks is reinforced by the expression of related molecular markers between these two cell populations. The aim of this study is to systematically review primary studies and identify circulating CSC markers in breast cancer patients. Methods and design Relevant observational studies evaluating the expression of circulating breast cancer stem cell markers through October 31, 2016, will be searched in PubMed, SCOPUS, Embase, ISI Web of Science, and Google Scholar with no restriction on language. Full copies of articles identified by the search and considered to meet the inclusion criteria will be obtained for data extraction and synthesis. Two quality assessment tools will be used for evaluating observational studies like case control, which are the Hoy et al. suggested tool and Newcastle-Ottawa Scale (NOS), respectively. Publication bias will be assessed by funnel plots or Eggerâ s test (i.e., plots of study results against precision), and data synthesis will be performed using Stata software (Stata Corp V.12, TX, USA).This systematic review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Discussion Detecting cancer stem cells in blood will help clinicians to monitor cancer patients by obtaining as many samples as needed with a non-invasive method and in any stages; it is not possible to repeat sampling on working on tissue samples. By identifying cancer stem cells early in blood, it will be possible to distinguish metastasis in early stages. Systematic review registration CRD42016043810
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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 | 0.049 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.015 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".