Impact of intermittent and maintenance chemotherapy on outcomes in metastatic colorectal cancer.
Bibliographic record
Abstract
6566 Background: With improved survival, clinicians managing metastatic colorectal cancer (mCRC) increasingly consider intermittent chemotherapy (IC) where patients receive a complete treatment break (TB) or maintenance chemotherapy (MC) where parts of a combination regimen are omitted. These modifications can improve quality of life (QoL) but their impact on outcomes is poorly understood. Methods: Using a population-based cohort of mCRC patients (pts) diagnosed from 2008 to 2010, we aimed to describe use of IC and MC and to compare median overall survival (mOS) in pts with and without these treatment modifications. IC was defined as TB intervals of ≥ 45 days without any cytotoxic agents while MC was defined as omission of one cytotoxic agent for ≥ 1 cycle. We examined outcomes using Kaplan-Meier methods, Cox regression, and propensity-score adjusted analyses. Results: Among 1249 mCRC pts, 279 (22%) underwent IC and had ≥ 1 TB, with median duration of 91 (IQR 63-158) days. TBs occurred at a median of 127 (IQR 57-234) days from initiation of a line of therapy and most pts (71%) only received 1 TB. In the 617 pts receiving a combination regimen, 120 (19%) had periods of MC, of which 68 (57%) had re-introduction of the omitted agent. On univariate analyses, patients undergoing IC (mOS 39 vs 23 months, P< 0.001) or MC (mOS 36 months vs 24 months, P= 0.0015) demonstrated better OS. In patients on MC, OS improvement was noted when the agent was restarted (mOS 41 months vs 24 months, P= 0.0073), but not without resumption (mOS 36 vs 24 months, P= 0.051). Using propensity scores and stratifying by treatment regimen, IC was not associated with altered OS (P > 0.05 across all regimens). In multivariate analysis adjusting for age, gender and regimen, MC was correlated with improved OS (HR 0.71, 95% CI 0.58-0.88, P= 0.002). When considering whether an omitted agent was re-introduced, MC with re-escalation (HR 0.68 95% CI 0.49-0.96, P= 0.027) was associated with improved OS, but not MC without re-escalation (HR 0.67 95% CI 0.50-0.91, P= 0.10). Conclusions: In patients with mCRC, IC and MC are reasonable options to maintain QoL and do not appear to negatively impact OS in carefully selected patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".