Coagulation markers as independent predictors of colorectal cancer aggressiveness
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is frequently associated with thrombosis with thrombotic events, such as deep vein thrombosis or pulmonary embolism, often correlate with poor clinical outcomes. Coagulation markers have been suggested as potential prognostic indicators for CRC severity. However, the relationship with clinicopathological characteristics in CRC remains unclear. PURPOSE: This study aims to examine the relationship between routine coagulation markers and clinicopathological characteristics in CRC patients. PATIENTS AND METHODS: A retrospective analysis was conducted on 100 patients with confirmed diagnosis of CRC, classified according to the 2018 edition of the American Joint Committee on Cancer Tumor/Node/Metastasis staging system for malignant tumors. Clinicopathological characteristics and routine coagulation tests including prothrombin time, and international normalized ratio, activated partial thromboplastin time, prothrombin activity, thrombin time, fibrinogen, d-dimer, platelet count, were evaluated. Spearman correlation was used to assess correlations with clinicopathological characteristics. Additionally, univariate and multivariate ordinal regression analysis were conducted to detect the independent predictors for CRC aggressiveness. RESULTS: Our data documents several associations between coagulation markers and cancer progression markers. Specifically, positive correlations were identified between fibrinogen and d-dimer levels and each of the following: carcinoembryonic antigen, carbohydrate antigen, tumor stage, node involvement, and metastasis. Regression analysis showed, d-dimer (OR = 1.102, p < 0.001) and fibrinogen (OR = 1.002, p < 0.001) are independent predictors of high-risk CRC cases. CONCLUSION: Fibrinogen and d-dimer may serve as independent predictive biomarkers for CRC aggression. Their clinical utility could support personalized treatment plans for CRC patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".