Modulation of Fluorouracil by Leucovorin in Patients With Advanced Colorectal Cancer: An Updated Meta-Analysis
Bibliographic record
Abstract
PURPOSE: The modulation of fluorouracil (FU) by folinic acid (leucovorin [LV]) has been shown to be effective in terms of tumor response rate in patients with advanced colorectal cancer, but a meta-analysis of nine trials previously published by our group failed to demonstrate a statistically significant survival difference between FU and FU-LV. We present an update of the meta-analysis, with a longer follow-up and the inclusion of 10 newer trials. PATIENTS AND METHODS: Analyses are based on individual data from 3,300 patients randomized in 19 trials on an intent-to-treat basis. Two trials had multiple comparisons, leading to a total of 21 pair-wise comparisons. FU doses were similar in both arms in 10 pair-wise comparisons, 15% to 33% higher in the FU-alone arm in six comparisons, and more than 66% higher in five comparisons. RESULTS: Overall analysis showed a two-fold increase in tumor response rates (11% for FU-LV v 21% for FU-LV v 11% for FU [corrected] alone; odds ratio, 0.53; 95% CI, 0.44 to 0.63; P <.0001) and a small but statistically significant overall survival benefit for FU-LV over FU alone (median survival, 11.7 v 10.5 months, respectively; hazards ratio, 0.90; 95% CI, 0.87 to 0.94; P =.004), which were primarily seen in the first year. We observed a significant interaction between treatment benefit and dose of FU, with tumor response and overall survival advantages of FU-LV over FU-alone being restricted to trials in which a similar dose of FU was prescribed in both arms. CONCLUSION: This updated analysis demonstrates, on a large data set, that FU-LV improves both response rate and overall survival compared with FU alone and that this benefit is consistent across various prognostic factors.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".