Are patients with primary glomerular disease at increased risk of malignancy?
Bibliographic record
Abstract
Over the past decade, several observational studies and case series have provided evidence suggesting a connection between glomerular diseases and the development of malignancies, with an estimated risk ranging from 5 to 11%. These malignancies include solid organ tumours as well as haematologic malignancies such as lymphoma and leukaemia. However, these risk estimates are subject to several sources of bias, including unmeasured confounding from inadequate exploration of risk factors, inclusion of glomerular disease cases that were potentially secondary to an underlying malignancy, misclassification of glomerular disease type and ascertainment bias arising from an increased likelihood of physician encounters compared with the general population. Consequently, population-based studies that accurately evaluate the cancer risk in glomerular disease populations are lacking. While it is speculated that long-term use of immunosuppressive medications and glomerular disease activity measured by proteinuria and estimated glomerular filtration rate may be associated with cancer risk in patients with glomerular disease, the independent role of these risk factors remains largely unknown. The presence of these knowledge gaps could lead to a lack of awareness of cancer as a potential chronic complication of glomerular disease, underutilization of routine screening practices in clinical care that allow early diagnosis and treatment of malignancies and underrecognition of modifiable risk factors to decrease the risk of de novo malignancies over time. This review summarizes the current evidence on the risk of cancer in patients with glomerular diseases, explores the limitations of prior studies and discusses methodological challenges and potential solutions for obtaining accurate estimates of cancer risk and identifying modifiable risk factors unique to GN populations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".