Bortezomib in Multiple Myeloma: Systematic Review and Clinical Considerations
Bibliographic record
Abstract
We conducted a systematic review to determine the appropriate use of bortezomib alone or in combination with other agents in patients with multiple myeloma (mm). We searched medline, embase, the Cochrane Library, conference proceedings, and the reference lists of included studies. We analyzed randomized controlled trials and systematic reviews if they involved adult mm patients treated with bortezomib and if they reported on survival, disease control, response, quality of life, or adverse effects. Twenty-six unique studies met the inclusion criteria. For patients with previously untreated mm and for candidates for transplantation, we found a statistically significant benefit in time to progression [hazard ratio (hr): 0.48, p < 0.001; and hr: 0.63, p = 0.006, respectively] and a better response with a bortezomib than with a non-bortezomib regimen (p < 0.001). Progression-free survival was longer with bortezomib and thalidomide than with thalidomide alone (p = 0.01). In non-candidates for transplantation, a significant benefit in overall survival was observed with a bortezomib regimen (hr compared with a non-bortezomib regimen: 0.61; p = 0.008), and in transplantation candidates receiving bortezomib, the response rate was improved after induction (p = 0.004) and after a first transplant (p = 0.016). In relapsed or refractory mm, overall survival (p = 0.03), time to progression (hr: 1.82; p = 0.000004), and progression-free survival (hr: 1.69; p = 0.000026) were significantly improved with bortezomib and pegylated liposomal doxorubicin (compared with bortezomib alone), and bortezomib monotherapy was better than dexamethasone alone (hr: 0.77; p = 0.027). Bortezomib combined with thalidomide and dexamethasone was better than either bortezomib monotherapy or thalidomide with dexamethasone (p < 0.001). In previously untreated or in relapsed or refractory mm patients, bortezomib-based therapy has improved disease control and, in some patients, overall survival.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".