A critical evaluation of quality of life in clinical trials of breast cancer patients treated with radiation therapy
Bibliographic record
Abstract
The aim of this study was to investigate the extent to which health-related quality of life (HRQOL) parameters have been reported in phase III trials with breast cancer patients (BCPs) who received radiation therapy (RT). We also examine the frequency and correlates of significant HRQOL gains. A systematic review was conducted. When HRQOL was a study endpoint, we extracted data on the instruments used for HRQOL analysis, assessing if there was formal statistical comparison between study groups and the results of such comparisons as reported by the authors of the studies. In result, 182 trials were included. HRQOL was considered as endpoint in 38 (20.8%) of the studies and it was used as primary endpoint in 10.9% of them. Of 22 trials that had a positive primary endpoint, 18 had a significant benefit in HRQOL, in favor of the experimental arm. Of 13 trials that had a negative primary endpoint, there were no differences in HRQOL among the study groups. With respect to HRQOL assessment, statistical methods and definition of timing of evaluation were described in 32 (84.2%) and 36 (94.7%) trials, respectively. In conclusion, HRQOL has been infrequently investigated in trials in BCP who received RT. Statistical methods and timing of evaluation were infrequently described with sufficient detail to be informative.
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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 | 0.213 | 0.480 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".