Macrocytosis as a predictor of response to capecitabine in solid cancers: A meta-analysis.
Bibliographic record
Abstract
e13080 Background: Capecitabine is an effective oral chemotherapy that is widely used in a number of solid cancers both as monotherapy or in combination with other anti-cancer drugs. It has been suggested that mean corpuscular volume (MCV) is associated with response to capecitabine. Methods: We searched PubMed for studies exploring the association between capecitabine and macrocytosis or MCV. We extracted the hazard ratios (HR) reporting progression-free (PFS) or overall survival (OS) data when comparing macrocytosis to normal/low MCV. If HR were not directly reported, we estimated them from survival plots using the Parmar method. HR were then pooled in a meta-analysis using generic inverse variance and random effects modeling. Results: Among the 13 identified studies, five were eligible for analysis, comprising a total of 446 patients. One study was a randomized trial and four were retrospective cohort studies. Mean patient age was 53 and cancer sites included breast (n = 226; 50%), colon (n = 131; 29 %) and stomach (n = 89; 19%). Capecitabine was used in combination with other drugs in 64% of patients. There was no association between macrocytosis and PFS (HR 0.91, 0.60-1.38, p = 0.65). Among the 3 studies reporting OS data, there was a significant negative association between macrocytosis and worse OS (HR 1.79, 1.38-2.34, p < 0.001). Conclusions: Macrocytosis in patients treated with Capecitabine was found to have no impact on PFS, but was associated with an inferior OS. This finding suggests that macrocytosis is more likely to be a prognostic factor rather than a predictive biomarker of response to capecitabine.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.057 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".