Impact of treatment on patient-reported pain and fatigue in early breast cancer patients receiving adjuvant radiotherapy.
Bibliographic record
Abstract
e12019 Background: Patients who receive radiotherapy (RT) for breast cancer often report pain and fatigue which contribute negatively to quality of life (QoL). We aim to identify demographic, treatment, and disease characteristics associated with pain and fatigue using the Edmonton Symptom Assessment Scale (ESAS). Methods: We identified all patients diagnosed with non-metastatic breast cancer from 2011 Jan-2017 Jun at the Odette Cancer Centre with at least one ESAS completed pre- and post-RT. Data on systemic treatment, RT, demographics, and disease stage were extracted. To identify factors associated with pain and fatigue pre- and post-RT and their changes, univariate and multivariable linear regression analysis was conducted. p < 0.05 was considered statistically significant. Results: This study included 1222 female patients (mean age 59 years old) who completed ESAS on average 28- and 142-days pre-RT (baseline) and post-RT respectively. In multivariable analysis, higher baseline pain scores associated with adjuvant chemotherapy (p < 0.001) and eventual receipt of locoregional (p = 0.026) or chest wall RT (p = 0.003), whereas higher baseline fatigue scores associated with higher disease stages (p = 0.001) and locoregional RT (p < 0.001). Post-RT symptom severity correlated only with locoregional RT (higher pain scores, p < 0.001). Reductions in pain was associated with adjuvant chemotherapy (p = 0.002) and chest wall RT (p = 0.031). Reduction in fatigue was associated with adjuvant chemotherapy (p = 0.011) and locoregional RT (p = 0.007) although both had higher pre-RT scores in univariate analysis. Conclusions: Patients at higher disease stages or who received chemotherapy or chest wall RT tended to experience more severe short-term morbidity. However, patients who received locoregional RT tended to have greater pain that persisted after RT completion compared to those who did not.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".