Assessing the prognostic utility of smoldering multiple myeloma risk stratification scores applied serially post diagnosis
Bibliographic record
Abstract
The Mayo-2018 smoldering multiple myeloma (SMM) risk score is used routinely in the clinical setting but has only been validated at diagnosis. In SMM patients, the progression risk decreases over time. However, the utility of applying risk stratification models after diagnosis is unknown. We retrospectively studied 704 SMM patients and applied the Mayo 2018 and IMWG-2020 risk stratification models at annual landmark timepoints up to 5 years post diagnosis. The Mayo-2018 and IMWG-2020 models reliably stratified patients based on progression risk when applied post diagnosis. The respective 2-year progression risk in Mayo-2018 high risk patients versus IMWG-2020 intermediate-high risk patients was 51% versus 62% at the 1-year landmark and 47% versus 45% at the 4-year landmark. We showed that patients categorized at Mayo-2018 high-risk at follow-up had a similar risk of progression if the baseline risk assessment was low-intermediate versus high-risk (HR 1.04, 95% CI 0.46-2.36, p = 0.931 at 5-year landmark). Patients migrating to a higher risk category during follow up had a higher progression risk compared to patients with stable/decreased risk categorization. Our findings support the use of these risk scores post-diagnosis and suggest that patients evolving to a high-risk category may benefit from early intervention therapeutic approaches.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".