Correlation between work productivity loss and EORTC QLQ-C30 and -BR23 domains from the MONALEESA-7 trial of premenopausal women with HR+/HER2− advanced breast cancer
Bibliographic record
Abstract
BACKGROUND: ET in premenopausal women with HR+/HER2- advanced breast cancer (ABC). The relationship between work productivity loss (WPL) and domains of European Organisation for Research and Treatment of Cancer Quality of Life (EORTC QLQ-C30) and the breast cancer (BC)-specific module (QLQ-BR23) has not been explored in ABC. In this post hoc analysis (data cutoff, November 30, 2018), we assessed the correlation between the WPL component of the Work Productivity and Activity Impairment: General Health (WPAI:GH) questionnaire and EORTC QLQ-C30/BR23 domains. METHODS: We analyzed EORTC and WPAI:GH data from 329 patients in both treatment arms of MONALEESA-7 who were employed during the trial. Separate univariable mixed-model repeated measures (MMRM) regression models were fitted for each domain, with WPL as dependent variable and each EORTC domain score as a single fixed-effect covariate. Linear and quadratic relationships were considered based on the Akaike information criterion. Next, two separate multivariable MMRM regression models were fitted with WPL a dependent variable and all QLQ-C30/BR23 domain scores as fixed-effect covariates. The strength of correlation between WPL and EORTC domains was assessed in terms of minimally important differences for the QLQ-C30/BR23 modules. RESULTS: Our univariable analysis showed that greater WPL was statistically significantly associated with lower levels of overall quality of life (QoL) and other functional domains and with higher levels of all symptomatic domains of the QLQ-C30/BR23 modules. Our multivariable analysis determined that this correlation was primarily driven by changes in QoL; physical, role, social, and future perspective domains; and BC-specific symptomatic domains. CONCLUSION: This analysis determined the QoL domains that correlate with WPL in premenopausal patients with HR+/HER2- ABC. These results may inform prognostic tools to identify and characterize patients with greater risk for WPL and help design interventional strategies to minimize WPL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".