Does the dose of leucovorin (LV) matter with 5-fluorouracil (5FU) in colorectal cancer (CRC)?
Bibliographic record
Abstract
708 Background: In June 2015 Cancer Care Ontario convened an expert panel to determine if there is an optimum LV dose in 5FU-LV combinations for the treatment of CRC. This request arose out of observed variation in LV dosages between some cancer centers. The research question was effect of LV dose on overall survival (OS), progression free survival (PFS), disease free survival (DFS), response rate (RR) and adverse events/toxicity, given a constant dose of 5FU. Methods: A systematic search was conducted for guidelines (GL) and comparative trials; eligibility included English language, with > 30 patients, that examined different doses of LV where dose of 5FU was not varied. Assessment of studies for inclusion was completed by 4 reviewers. Results: We identified 5 GL, 0 systematic reviews and 12 trials that defined a LV dose in combination with 5FU. None of the GL informed an optimal dose of LV. RR was assessed in 10 trials; 4 showed trends to higher RR with higher LV dose, but differences were not statistically significant (SS) between arms. PFS or DFS was reported in 6 trials and was similar between arms. Time to recurrence reported in one trial that included bevacizumab (BV) was longer in the high dose LV group that was SS. OS was addressed in 10 studies: no difference found in 7 studies; in one RCT OS was longer with the higher dose LV 55 vs. 45 months (p not reported); in one retrospective study OS was 23 vs. 20 months in favor of high dose LV (p not reported); one study of LV and BV had longer OS vs. lower dose LV at 26 vs. 21 months (SS). Toxicity: higher dose LV was associated with greater toxicity in 3 of 4 studies that reported stomatis, and increased rates of diarrhea in 9 of 11 trials. Meta-analysis was not appropriate as studies were too heterogeneous. Conclusions: There is no convincing evidence to identify an optimum dose of LV to be used in 5-FU/LV combinations. Amongst studies that did show a difference the trend was improved survival in favor of the higher dose. Similarly, differences in toxicities when identified were consistently greater with the higher dose LV. The expert panel concludes that the existing literature provides insufficient data to suggest that chemotherapy protocols should deviate from standard protocol doses.
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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 | 0.024 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".