Mass Spectrometric Serum Markers of Colorectal Cancer
Bibliographic record
Abstract
Purpose: The purpose of this study was to discover biomarkers in serum that can differentiate patients with colorectal cancer (CRCa) from healthy controls and patients with benign colorectal disease. CRCa is the third leading type of cancer, with approximately 150000 new cases and 56000 deaths estimated in the US in 2005. Early detection of CRCa can significantly improve survival rates versus diagnosis at later disease stages. Current colorectal cancer screening tools suffer from a variety of shortcomings, which can include poor sensitivity, poor specificity, expense, patient discomfort and limited application to screen patients at risk. Methods: Proteomic screening of clinical serum samples by mass spectrometry was conducted to discover and characterize components of these samples for their ability to differentiate CRCa from non-CRCa patients. Results: 27 discrete proteins/peptides were discovered that could differentiate CRCa patient samples from healthy control and/or benign colorectal disease patient samples. Application of these serum components as biomarkers in a variety of single-model classification algorithms with 10-fold cross validation showed sensitivity/specificity of approximately 80%/80%. The use of bagging met-analysis with 10-fold cross validation gave sensitivity/specificity of approximately 85%/85%. Conclusions: The discovery of serum proteins and peptides with the potential to help detect and diagnose CRCa is presented.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".