Multiple myeloma in Latin America: Are we moving at the same pace as other regions?
Bibliographic record
Abstract
Multiple myeloma (MM) is a plasma cell disorder that has recently experienced a dramatic improvement in clinical outcomes mainly due to the advent of novel drugs and the implementation of better supportive care strategies (1). Recently available combinations of proteasome inhibitors (PIs), immunomodulators (IMiDs), and monoclonal antibodies like daratumumab and isatuximab are shifting the MM therapeutic landscape (2-4). Nonetheless, MM still remains incurable, with an estimated 5-year survival of 55% and an estimated death rate of 2% in 2021 (% of all cancer deaths) (5). In Latin America (LATAM), the Hemato-Oncology Latin America (HOLA) study was recently designed to evaluate the epidemiology of hematologic malignancies in the real-world setting (6). In some countries from LATAM, stage III MM was the most frequently observed disease stage, with Mexico (62.5%), Chile (60.0%), Brazil (49.3%), and Colombia (45.8%) being the regions where MM patients presented with a more advanced stage. This is in contrast with the original report of revised staging criteria, where stage III MM represented 22% of the cases (7). In the entire cohort of the HOLA study, 497 patients with MM (32.7%) underwent autologous stem-cell transplantation (ASCT); however, the proportion of patients submitted to ASCT varied among countries, ranging from 3% to 69%. The 497 patients who underwent ASCT had received induction chemotherapy predominantly based on thalidomide (151; 30.4%) and bortezomib (125; 25.2%) regimens. This is quite different when compared to countries like Canada, where in a recent study by Mian et al. (8), a total of 5,154 patients with MM were identified, among which 3,030 patients (58.8%) received an upfront ASCT and 2,124 (41.2%) did not. Bortezomib and lenalidomide were the most frequently used agents (>50%) in first- and second-line treatment, respectively, in both the ASCT and non-ASCT cohorts. In Colombia, Abello et al. (9) reported on the outcomes of 890 patients with MM from a real-world registry. Most patients in this group received bortezomib and thalidomide-based therapies with a 65% response rate for CyBorD (cyclophosphamide, bortezomib, and dexamethasone) and 79% for VTD (bortezomib, thalidomide, and dexamethasone), which is in contrast with 78.1-84.3% and 85-94% reported in other series treated with similar regimens (10-12).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".