Double trouble : exploring the link between systemic lupus erythematosus and cancer
Bibliographic record
Abstract
Concern exists that individuals with systemic lupus erythematosus (SLE) have increased susceptibility to cancer compared with the general population. My thesis contains five chapters, each presenting a different perspective on the issue of cancer in SLE. Although past literature has suggested an increased risk of cancer in SLE, conclusions from earlier studies were not uniform. I review this earlier data in the first chapter of my thesis. The absence of adequate data regarding malignancy risk in SLE meant that a large, multicentre effort was needed. We have recently completed this multi-centre cohort study, comparing cancer risk in SLE relative to the general population. In the second chapter I present my analyses of these data, which confirm an increased risk of cancer in SLE. The risk is particularly evident for non-Hodgkin's lymphoma (NHL), where an almost four-fold increased risk is estimated. A potential bias which has been invoked as a possible explanation for the associations between cancer and other chronic disease exposures has been variously called "misclassification", "detection" or "surveillance" bias. If this bias, related to a potential for greater scrutiny for cancer in SLE patients, does exist, one could expect that cancers in SLE patients are diagnosed at earlier stages than in the general population. I examine this in the third chapter, presenting my work that does not support the presence of "surveillance" bias in the results from the multi-centre SLE cohort study. In the fourth chapter, I describe the demographic factors, subtypes, and survival of the NHL cases that arose in the multicentre SLE cohort sample. The data suggest that aggressive NHL subtypes and poor outcome are common in SLE. Though the pathogenesis of cancer in SLE is unknown, one theory is that exposure to immunosuppressive medications is a factor. Although definitive evidence is not available, I present, in the final chapter, my findings within the Montreal General Hospital SLE cohort, where immunosuppressive exposure was associated with abnormalities on cervical cancer screening (Pap) tests. In summary, our work demonstrates an increased risk of cancer in SLE; this is not likely due to surveillance bias. Immunosuppressive exposure may be associated with abnormal Pap tests; further work will determine whether immunosuppressives confer risk for other neoplastic events in SLE, particularly NHL.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".