Lapatinib in HER2+ early breast cancer: Quality of life analysis.
Bibliographic record
Abstract
604 Background: TEACH, a randomized, double-blind, multi-center, phase III study evaluated the efficacy and safety of 12 months lapatinib (N=1571) versus placebo (N=1576) in women with HER2+ EBC. Patients receiving lapatinib more often reported treatment-related AEs (87% vs 47%) in the Intent-to-treat (ITT) population. The comparison of recurrence-free survival was not significant (p=.053). This analysis focuses on QOL in the ITT. Methods: Participants on TEACH completed the Short Form-36 version 2 (SF-36v2) at baseline and every 6 mo. for 24 mo. SF-36v2 captures physical, social, mental, emotional domains of patient perceptions; summary scores of physical and mental components are derived from domain scores using norm based scoring method. Changes in the domain and summary scores were compared between two groups using ANCOVAs. Missing post-baseline data were imputed using the last observation carried forward method. A clinically relevant change in scores was defined as a 3-5 point change. Results: For randomised patients who completed the SF-36, QOL scores decreased relative to baseline for all domains and summary scores across all assessments and treatment arms, however the decreases were small and not clinically meaningful. There were no statistically significant (p< 0.05) or clinically meaningful differences between arms for the summary scores relative to baseline (Table). Conclusions: In HER2 positive EBC one year treatment with lapatinib is associated with a significant increase in AEs but it does not have a significant detrimental impact on QOL when compared to placebo. [Table: see text]
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".