Bibliographic record
Abstract
Multiple myeloma (MM) is the second most common hematologic cancer resulting from proliferation and accumulation of abnormal plasma cells (myeloma cells) with a preferential homing in the bone marrow. It causes significant morbidity including lytic bone lesions, renal insufficiency, anemia, and infections, to name just a few. Although MM remains largely incurable, it is a chemo-sensitive disease. The use of high-dose intravenous melphalan (100-140 mg/m2) in the treatment of MM was first studied almost 4 decades ago.Subsequently, the dose of melphalan was increased and was followed by autologous hematopoietic stem cell to decrease the aplasia-associated toxicity. Results from phase 3 studies comparing chemotherapy alone to chemotherapy followed by high-dose melphalan and autologous stem cell transplantation appeared in the mid-90s with the publication of the IFM-90 study demonstrating significant clinical benefits on response rate, event-free survival and even overall survival in a cohort of two hundred previously untreated patients under the age of 65 years. This landmark study was followed by confirmatory studies in the early 2000’s. Within the last 2 decades, although improvement in the treatment of transplant-eligible patients is mostly the result of better induction regimens and due to the addition of maintenance therapies, autologous stem cell transplantation remains a cornerstone treatment for MM patients. Indeed, despite novel and more effective treatments for MM, autologous stem cell transplantation continues to demonstrate clinical benefits (Table 1). Moreover, tandem autologous transplantation has demonstrated progression-free survival and overall survival benefits for some patients with poor risk cytogenetics. In 2022, with better knowledge of MM, awareness of potential consequences of high-dose melphalan and with novel and more effective treatment modalities, the role of autologous stem cell transplantation is certainly becoming a question for debate. The purpose of this article is to present the pros and cons of autologous stem cell transplantation in our Canadian reality (Figure 1). This article aims to better assess its role as a therapeutic option considering our health system’s limited resources in which many novel drugs will not be available/accessible in Canada for several more years to come.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".