Should de-escalation of bone-targeting agents be standard of care?
Bibliographic record
Abstract
Thank you for the letter from Liu et al. regarding our 2015 review [1.Ibrahim M.F.K. Mazzarello S. Shorr R. et al.Should de-escalation of bone-targeting agents be standard of care for patients with bone metastases from breast cancer? A systematic review and meta-analysis.Ann Oncol. 2015; 26: 2205-2213Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar]. While we appreciate their view that our review may have changed broader clinical practice, while flattered, we are unaware of evidence supporting this premise. Our conclusions indicate that our results ‘appear’ to show no difference in events with de-escalation of bone-targeted agents but that results of ongoing studies were eagerly anticipated. We are happy to address the authors’ perspectives. First, they queried why we did not include data from Lipton’s subsequent analysis [2.Lipton A. Steger G.G. Figueroa J. et al.Extended efficacy and safety of denosumab in breast cancer patients with bone metastases not receiving prior bisphosphonate therapy.Clin Cancer Res. 2008; 14: 6690-6696Crossref PubMed Scopus (138) Google Scholar] of his prior study [3.Lipton A. Steger G.G. Figueroa J. et al.Randomized active-controlled phase II study of denosumab efficacy and safety in patients with breast cancer-related bone metastases.J Clin Oncol. 2007; 25: 4431-4437Crossref PubMed Scopus (321) Google Scholar]. As our review evaluated only de-escalation of the same dose of bone-targeted agent, we compare the data for denosumab 180 mg q4-weekly with q12-weekly. The later publication contains no new data for this question. Second, the authors suggest the choice of a random effects model for meta-analyses based on the low values of I2 may be more prone to find evidence of no differences between interventions. However, we feel variations between studies in several capacities exist, justifying use of random effects models. Numerically, random effects meta-analyses with no statistical heterogeneity will produce analogous estimates to those from fixed effects meta-analyses, and this was the case for five of six meta-analyses presented (for the remaining outcome, clinical interpretations remain unchanged). The authors also point to our inclusion of data from abstracts as problematic. We agree abstracts represent a less transparent account of studies than full publications, and we previously noted this limitation. We acknowledge some of the inaccuracies noted. The authors noted that our search included errors in certain lines. We have confirmed with our librarian that these transcription errors were not part of the formal search run. The authors are correct that the study characteristics listing for the trial by Amoradi [4.Amadori D. Aglietta M. Alessi B. et al.Efficacy and safety of 12-weekly versus 4-weekly zoledronic acid for prolonged treatment of patients with bone metastases from breast cancer (ZOOM): a phase 3, open-label, randomised, non-inferiority trial.Lancet Oncol. 2013; 14: 663-670Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar] should indicate 12–15 months of prior zoledronate; fortunately this will not impact findings from meta-analyses. The authors mentioned that they were unable to find the data for breast cancer patients in Fizazi et al. [5.Fizazi K. Lipton A. Mariette X. et al.Randomized phase II trial of denosumab in patients with bone metastases from prostate cancer, breast cancer, or other neoplasms after intravenous bisphosphonates.J Clin Oncol. 2009; 27: 1564-1571Crossref PubMed Scopus (467) Google Scholar], nor within the additional results within www.clincialtrials.gov. We have reviewed these sources again and confirm that the authors are correct. While the trial’s registration record provides additional results, this does not include outcomes by tumour type. Lastly, the authors are correct that there is a discrepancy in the SRE-related data provided in the main text and the abstract; the confidence interval provided in the results section is correct. We thank the authors for their input and apologize for these inaccuracies to readers. While we vary in opinion regarding the extent to which these inaccuracies effect the quality and conclusion of our review, we can report that (as per our PROSPERO registration from June 2017) we are in the latter stages of updating our 2015 review with new data available. We encourage all systematic reviewers to register in PROSPERO, and we will ensure that these adjustments are incorporated in our update. We are also leading a research study addressing this important question (NCT02721433), and we look forward to sharing this data in the future, providing additional updates of meta-analyses for clinicians and researchers. None declared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".