Emerging Treatments in Recurrent and Metastatic Colorectal Cancer
Bibliographic record
Abstract
Metastatic colorectal cancer (mCRC) is a prevalent disease for which many new therapies have been developed over the past decade. Currently, standard of care chemotherapeutic regimens for mCRC include doublet cytotoxic chemotherapy with or without the anti-vascular endothelial growth factor (VEGF) monoclonal antibody bevacizumab, anti-epidermal growth factor receptor (EGFR) monoclonal antibodies such as cetuximab and panitumumab with or without chemotherapy, and single-agent cytotoxic chemotherapy or targeted therapy for patients intolerant of combination regimens. Recent studies have investigated the efficacy of triplet cytotoxic chemotherapeutic regimens, bevacizumab in combination with chemotherapy beyond first-line therapy disease progression, dual anti-VEGF and anti-EGFR antibody therapy, and the more novel agents ziv-aflibercept and regorafenib for treatment of mCRC. Furthermore, molecular profiling of CRC has identified several genetic alterations for which targeted therapies are currently being developed. Optimal drug combinations and treatment sequences have yet to be defined, but an expanding armamentarium of therapies with which to treat CRC offers a promising future.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".