Associations Between Patient Characteristics and Progression to Multiple Myeloma Among Patients With Monoclonal Gammopathy of Undetermined Significance: A Systematic Review
Bibliographic record
Abstract
Monoclonal gammopathy of undetermined significance (MGUS) is a pre-malignant condition of multiple myeloma (MM). Evidence suggested old age, black race, male gender, and obesity as risk factors for MGUS development; however, whether they are associated with an increased risk of progression to MM among patients with MGUS is unclear. A systematic search of PUBMED and EMBASE for cohort studies investigating the association between age/race/gender/obesity and progression to MM. We used the Newcastle-Ottawa Scale (NOS) to assess the methodologic quality of the included studies. Summary risk ratios were calculated using random-effects models. We identified 24 publications, of which 17 articles were included in the main analyses. Overall, the quality of the studies was fair (mean NOS = 5.5). Our meta-analyses showed that old age was positively associated with the risk of the MGUS-MM progression (risk ratio: 2.38; 95% confidence interval [CI] 1.59-3.57), while race was not statistically significantly associated with the risk (blacks vs whites: 1.09; 95% CI: 0.77-1.54). Males had a lower risk of MGUS-MM progression, compared to females (risk ratio: 0.70; 95% CI 0.50-1.0; P-value = .048). High body mass index was significantly associated with an increased risk of MGUS-MM progression (risk ratio: 1.32; 95% CI 1.12-1.57). Based on extant research, old age, female sex, and obesity may be implicated in MGUS-MM progression. However, several studies which found an insignificant association between age/gender and progression did not report the risk estimates. Publication bias exists and our risk estimates may be overestimated. More studies are warranted to confirm our findings.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".