Bibliographic record
Abstract
1) Background: recent evidence suggests that long low-dose capecitabine regimens have a synergistic effect with endocrine therapy as aromatase inhibitors (AIs), and might increase overall survival for hormone-receptor-positive, HER2-negative, metastatic breast cancer compared to both treatments.We performed a retrospective study to confirm the efficacy and expand the safety data for capecitabine plus AI (a combination henceforth named XELIA) for this indication.(2) We conducted a single-center retrospective cohort study of 163 hormone receptor-positive metastatic breast cancer patients who received either the XELIA regimen, capecitabine, or an aromatase inhibitor (AI) as single agents in first-line treatment.The primary endpoint was progression-free survival, and the secondary endpoints were overall survival, best objective response, and toxicity incidence.(3) Results: the median progression-free survival for patients receiving XELIA, AI, and capecitabine was 29.37 months (20.91 to 37.84; 95% CI), 20.04 months (7.29 to 32.80; 95% CI) and 10.48 (8.69 to 12.28; 95% CI), respectively.The overall response rate was higher in the XELIA group (29.5%) than in the AI (14.3%) and capecitabine (9.1%) groups.However, the differences in overall survival were not statistically significant.Apart from hand-foot syndrome, there were no statistically significant differences in adverse events between the groups.(4) Conclusions: this retrospective study suggests that progression-free survival and overall response rates improved with the XELIA regimen compared to use of aromatase inhibitors and capecitabine alone.Combined use demonstrated an adequate safety profile and might represent an advantageous treatment in places where CDK 4/6 is not available.Larger studies and randomized clinical trials are required to confirm the effects shown in our study.
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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 | 0.005 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.838 | 0.523 |
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".