A Meta-Analysis of the Relationship between Testicular Microlithiasis and Incidence of Testicular Cancer.
Bibliographic record
Abstract
PURPOSE: There are many recent observational studies on testicular microlithiasis (TM) and risk of testicular cancer. Whether TM increases the risk of testicular cancer is still inconclusive. The objective of this updated meta-analysis was to synthesize evidence from clinical observational studies that evaluated the association between TM and testicular cancer. MATERIALS AND METHODS: We identified eligible studies by searching the PubMed, Embase and Cochrane Library before March 2014. Adjusted relative risks (RR) with 95% confidence interval (CI) were calculated using random-or fixed-model. RESULTS: A total of 14 studies involving 35,578 participants were included in the meta-analysis. On the basis of the Newcastle Ottawa Scale systematic review, eleven studies were identified as relatively high-quality. TM was strong association with an increased incidence of testicular cancer (RR = 12.70, 95% CI: 8.18-19.71, P < .001), with significant evidence of heterogeneity among these studies (P for heterogeneity < .001, I2 = 82.1%). The subgroup and sensitivity analysis confirmed the stability of the results and no publication bias was detected. CONCLUSION: The present meta-analysis suggests that TM is significantly associated with risk of testicular cancer. More researches are warranted to clarify an understanding of the association between TM and risk of testicular cancer.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".