Metastatic Colorectal Cancer in the Era of Personalized Medicine: A More Tailored Approach to Systemic Therapy
Bibliographic record
Abstract
Colorectal cancer is the second most common malignancy diagnosed in Canada. Despite declining incidence and mortality rates in recent years, there is still a significant number of cases that are metastatic at presentation. Fluoropyrimidine-based chemotherapy was the backbone of colorectal cancer treatment, but the addition of irinotecan and oxaliplatin to form combination regimens has significantly improved overall survival. In the past decade, the development of novel biologic agents including therapies directed against vascular endothelial growth factor and epidermal growth factor receptor has further altered the landscape of metastatic colorectal cancer treatment. However, clinical trials have demonstrated that not all patients respond to these therapies similarly and consideration must be given to individual patient- and tumor-related factors. A more tailored and biomarker driven approach to treatment selection can optimize outcomes and avoid unnecessary adverse effects. In this review article, we offer a comprehensive overview of the panel of clinical- and tumor-associated characteristics that influence treatment decisions in metastatic colorectal cancer and how this sets the foundation for a more personalized treatment strategy in oncology.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".