De-escalated administration of bone-targeted agents in patients with breast and prostate cancer—A survey of Canadian oncologists
Bibliographic record
Abstract
OBJECTIVE: Questions remain regarding the optimal use of bone-targeted agents in patients with metastatic bone disease. The purpose of this study was to assess current clinical practice regarding the use and administration of bone-targeted agents by Canadian oncologists in patients with metastatic breast and prostate cancer. METHODS: A survey was designed to explore; bone-targeted agent use in metastatic bone disease, variability in the choice and the frequency of administration of these agents. Opinions were sought on potential outcomes for future trials. RESULTS: A total of 193 clinicians were contacted and 90 completed our survey (response rate 49% after adjustment for inactivity). Survey respondents were medical oncologists (71.1%), radiation oncologists (21.1%) and urologists (7.8%). The findings suggest that once bone-targeted agents are started they are rarely discontinued. More agents are used in breast cancer than in prostate cancer. There was considerable interest in performing studies of de-escalated therapy in both breast and prostate cancer. Physicians requested (86%) that the primary study endpoint be the occurrence of skeletal related events and not biomarker driven. CONCLUSIONS: Despite clinical practice guidelines and widespread use, significant areas of clinical equipoise with respect to use of bone-targeted agents exist. Findings from this survey suggest that physicians are interested in de-escalated therapy for both breast and prostate patients. However, the use of multiple agents in breast cancer and the desire for skeletal related events to be the primary endpoint means that very large randomized studies will be required.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".