Uniquely devoted to the Canadian myeloma community
Bibliographic record
Abstract
Although not often openly acknowledged, “cure versus control ” is the dominant philosophical difference behind many of the strategies, trials, and debates related to the management of myeloma. Should we treat patients with myeloma with multidrug, multitransplant combinations with the goal of potentially curing a subset of patients, recognizing that the risk of adverse events and effect on quality of life will be substantial? Or should we address myeloma as a chronic incurable condition with the goal of disease control, using the least toxic regimens, emphasizing a balance between efficacy and quality of life, and reserving more aggressive therapy for later? To be sure, if cure were known to be possible (with a reasonable probability) in myeloma, it would undoubtedly be the preferred therapeutic goal of most patients and physicians. But this is not the case. Myeloma is generally not considered a curable disease; however, new definitions of cure have been suggested, including operational cure, which is defined as a sustained complete response (CR) for a prolonged period. (1,2) Cure versus control is debated because the strategies currently being tested are not truly curative but rather are intended to maximize response rates in the hope that they will translate into an operational cure for a subset of patients. For decades, the treatment of myeloma was restricted to conventional chemotherapy with alkylators
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.101 | 0.014 |
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".