Incidence, consequences and treatment of bone metastases in breast cancer patients—Experience from a single cancer centre
Bibliographic record
Abstract
BACKGROUND: There is a paucity of literature about the benefits of bone-targeted agents for breast cancer patients with bone metastases treated in the non-trial setting. We explored the incidence, consequences, and treatment of bone metastases at a single cancer centre. METHODS: Electronic records of metastatic breast cancer patients were reviewed and pertinent information was extracted. RESULTS: Of 264 metastatic breast cancer patients, 195 (73%) developed bone metastases. Of these patients, 176 were eligible for analysis. Median age at bone metastases diagnosis was 56.9 years (IQR 48-67) and initial presentation of bone metastases included asymptomatic radiological findings (58%), bone pain (40%), or a SRE (12.5%). Most patients (88%) received a bone-targeted agent, starting a median of 1.5 months (IQR 0.8-3.30) after bone metastasis diagnosis. 62% of patients had ≥1 SRE. The median time from bone metastasis diagnosis to first SRE was 1.8 months (IQR 0.20-8.43 months). Median number of SREs per patient was 1.5 (IQR 0-3). Overall, 26.8% of all SREs were clinically asymptomatic. Within the entire cohort, 51% required opioids and 20% were hospitalized due to either an SRE or bone pain. CONCLUSIONS: Despite extensive use of bone-targeted agents, the incidence of SREs remains high. Nearly half of SREs occur prior to starting a bone-targeted agent. Use of opioids and hospitalizations secondary to bone metastases remain common. More effective treatment options are clearly needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".