Emerging Real-World Treatment Patterns and Clinical Outcomes of Multiple Myeloma in Argentina and Brazil: Insights from the TOTEMM Study in the Private Healthcare Sector
Bibliographic record
Abstract
(TOTEMM) was a database study (2018-2024) of newly diagnosed transplant-ineligible patients with MM in Argentina (TOTEMM-A) and Brazil (TOTEMM-B) in a private healthcare setting. In TOTEMM-A (n = 72) and TOTEMM-B (n = 892), 37 and 92 different drug regimens were reported, respectively. In each country, treatment duration reduced across lines of therapy (LOT) (TOTEMM-A: range, 6.2-3.4 months; TOTEMM-B: range, 4.4-3.5 months); attrition rates increased across LOT (TOTEMM-A: range, 52.8-86.1%; TOTEMM-B: range, 41.9-88.0%); triplet regimens (mainly bortezomib based) were used most frequently in first-line (1L); >75% relapsed within 12 months, regardless of the drug prescribed; over 90% of relapses occurred between 1L and second-line, and up to half of patients were rechallenged with the same drug; >65% of patients experienced disease progression after 1L; and the 1- to 5-year adjusted cumulative risk of progression or death increased across LOT (TOTEMM-A: range, 47.1-88.5%; TOTEMM-B: range, 40.4-91.7%). The rapid and marked progression underscores the urgent need for novel treatments and regimens.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".