Use of FOLFOXIRI Plus Bevacizumab and Subsequent Therapies in Metastatic Colorectal Cancer: An Age-Stratified Analysis
Bibliographic record
Abstract
BACKGROUND: Treatment recommendations for metastatic colorectal cancer (mCRC) do not differ by age group; nevertheless, aggressive multiagent chemotherapy comprising FOLFOXIRI+bevacizumab (triplet+bev) is routinely administered in younger patients. This study analyzed real-world data on index triplet+bev use and subsequent systemic therapies. MATERIALS AND METHODS: This retrospective, observational cohort study was conducted in patients aged ≥ 18 years with mCRC, who were initiated on triplet+bev. Data were derived from the Optum de-identified electronic health record dataset. RESULTS: Of 36,056 patients, 14%, 36%, and 50% were aged 18-49, 50-64, and ≥ 65 years, respectively. During the study period (2010-2021), triplet+bev use increased in patients aged 18-49 years (1%-4%) but remained at approximately 3% and 1% in patients aged 50-64 and ≥ 65 years, respectively. Patient demographics and clinical characteristics varied slightly; of patients receiving triplet+bev (n = 921) versus nontriplet+bev (n = 35,132) most were male (57% vs. 52%), resided in the Midwest (54% vs. 49%) and Northeast (18% vs. 14%) US regions, and had secondary malignancies (86% vs. 73%). Following triplet+bev, most patients received subsequent therapies (including continued triplet component therapies; 97%) or subsequent "new" therapies (therapies that did not include any agents comprising triplet+bev; 57%), most frequently EGFR inhibitors (28%) and regorafenib (21%), with a similar trend among all age groups. CONCLUSIONS: Overall, this study shows that younger patients with mCRC are more likely to receive first-line triplet+bev. These results also reveal that nonchemotherapy options are often used beyond first-line triplet chemotherapy for patients with mCRC.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".