The Effect of Breast Cancer Subtype on Symptom Improvement Following Palliative Radiotherapy for Bone Metastases
Bibliographic record
Abstract
AIM: To assess the relationship between breast cancer subtypes and patient-reported outcomes (PRO) following palliative radiotherapy for bone metastases. MATERIALS AND METHODS: Prospectively collected PRO for all breast cancer patients treated with palliative, bone metastasis-directed radiotherapy from 2013 to 2016 in the province of British Columbia were analysed. The PRO questionnaire scored pain severity, level of function and symptom frustration at baseline and at 3-4 weeks following palliative radiotherapy using a 12-point scale. The primary outcome was the rate of overall response (any improvement in score); the secondary outcome was the rate of complete improvement in PRO (final PRO score of 0). Multivariate logistic analysis was used to compare response rates between molecular subgroup approximations of luminal A (LumA), luminal B (LumB), HER2-enriched (HER2) and triple negative (TN), as defined by grade and immunohistochemical staining. RESULTS: There were 376 patients who underwent 464 courses of palliative radiation for bone metastases. Subtypes included: 243 LumA, 146 LumB, 46 HER2 and 29 TN. There were 216 multifraction radiotherapy courses (median dose 20 Gy) and 248 single-fraction radiotherapy courses (median dose 8 Gy). The overall response rate was 85% and the complete response rate was 25%. In comparison with LumA breast cancers, TN breast cancers were associated with a lower rate of overall response (69% versus 86%, P = 0.021) and a lower rate of complete response (10% versus 28.8%, P = 0.045) on multivariate analyses. CONCLUSION: Patients with TN breast cancer have lower rates of pain, function and symptom frustration improvement following palliative radiation for bone metastases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".