Type 2 diabetes mellitus does not increase the risk of multiple myeloma: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Epidemiological studies have shown that patients with type 2 diabetes mellitus (T2DM) are at a higher risk of secondary tumors. However, no consensus has been made about whether T2DM can increase the risk of multiple myeloma (MM). Methods: We searched the databases of PubMed, Cochrane Library and EMBASE and cross-checked the bibliography. Data quality was assessed using the Newcastle-Ottawa scale (NOS). Heterogeneity was calculated as the odds ratio (OR) using a random-effects model. Data were analyzed using Stata version 12.0 software. Results: A total of 13 articles were selected into this meta-analysis. Initially, we found that diabetic patients had a higher risk of myeloma than non-diabetic patients (OR =1.60, 95% CI: 1.13–2.26, I2=98%, P=0.000). But the data in these articles were highly heterogeneous (I2>75%). Therefore, eight of the included articles showed a moderate heterogeneity (I2=71.6%). We used Galbraith heterogeneity map to analyze the causes of heterogeneity. Two articles with high heterogeneity were excluded. Then, we found the heterogeneity of the left six articles was reduced from moderate to mild (I2=45.9%, P=0.100). The final results of this meta-analysis showed that T2DM was not a risk factor for increased incidence of MM (OR =1.05, 95% CI: 0.83–1.33, I2=45.9%, P=0.100). Also, the subgroup analysis (case-control studies vs. cohort studies) showed no statistical difference (OR =1.19, 95% CI: 0.76–1.85, I2=1%, P=0.364; OR =1.00, 95% CI: 0.75–1.33, I2=71.2%, P=0.031; respectively). Conclusions: T2DM is not a risk factor for the increased incidence of MM, a finding that should be validated with more strictly designed randomized controlled trials (RCTs).
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.051 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".