Real-Life Effectivity of Dose Intensity Reduction of First-Line mFOLFIRI-Based Treatment of Metastatic Colorectal Cancers: Sometimes Less Is More
Bibliographic record
Abstract
Aim: The key purposes of the treatment of metastatic malignancies are to extend survival and maintain the quality of life. Recently it has been emphasized in the scientific literature that the maintenance of maximal dose intensity is not always beneficial. Method: We examined the effectiveness of first-line mFOLFIRI-based treatments used in mCRC indication in 515 patients, treated between 1 January 2013 and 31 December 2018 at the Department of Oncotherapy of the University of Pécs, on a basis of real-world retrospective data analysis. We studied the effect of decreased dose intensity treatment modifications on patient survival. Results: 45% of all patients achieved the optimal relative dose intensity (RDI) of 85%, and the median progression-free and overall survival (mPFS, mOS) were 199 and 578 days, compared to 322 and 743 days, (mPFS p < 0.0002, 1 y (year) PFS OR (odds ratio) 0.39 (95% CI: 0.26−0.56) and mOS p = 0.0781, 2 yrs OS OR 0.58 (95% CI: 0.39−0.85), respectively) in the group of patients not achieving the RDI of 85%. Conclusions: Decreased dose intensity did not reduce the effectiveness of treatment; in fact, there was a significant improvement in most of the analyzed parameters. The option of reduced dose intensity, which shows the same or even better results with less toxicity, should definitely be considered in the future palliative treatment of mCRC 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".