Genomic profiling for copy number changes in plasma of ovarian cancer patients – a new era for cancer diagnostics?
Bibliographic record
Abstract
A blood test that can detect human malignancy with high clinical sensitivity and specificity is highly desirable. To achieve this, a tumor marker is needed that correlates with tumor burden and that can be measured with high analytical sensitivity and specificity. Over the past decades, a number of different types of tumor markers have emerged, including proteins such as enzymes, glycoproteins, and oncofetal antigens. Besides proteins, genetic abnormalities such as mutations, amplifications, and circulating tumor DNA have served as tumor markers. Despite the diversity of such biomarkers, their acceptance and implementation into routine clinical practice requires that their use results in improvements in patient outcome. Current tumor markers used in the clinic have limited utility. As such, innovative approaches to identifying tumor markers are highly desirable and one such approach may be to look for sub-chromosomal changes in the blood of patients with ovarian cancer, as is routinely performed in prenatal screening.Please see related article: http://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-016-0667-6.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".