Comparing The Results Of Bisphosphonate Use In Clinical Trials With Actual Practice
Bibliographic record
Abstract
Multiple randomized clinical trials have confirmed the ability of bisphosphonates to reduce or delay skeletal related events (SREs) when given in combination with either hormonal or chemotherapy to breast cancer patients with bone metastases. The use of bisphosphonates has therefore become a standard of care for the secondary prevention of skeletal complications in these patients. While the hazard ratios of benefit are impressive for the reduction and delay of SREs it is however important to appreciate that the patients enrolled onto these trials were restricted to those with a life expectancy of at least six months and were frequently over represented with patients with bone only disease. These patient populations are those who were most likely to derive maximum benefit from treatment. Patients treated in actual clinical practice are a more heterogeneous population with often an inherently poorer prognosis. It is likely that they do not derive the same degree of benefit. This paper reviews the use and outcomes of treatment with bisphosphonates in a cohort of 190 breast cancer patients with bone metastases treated at three Canadian centers. It highlights the differences between clinical practice and trial populations and discusses why we cannot estimate the true order of magnitude of benefit for treatment with bisphosphonates in an off trial setting. This can have important implications for individual patient care and also for pharmacoeconomic evaluation of these agents.
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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 | 0.441 | 0.752 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".