Bibliographic record
Abstract
Objectives. (1) To estimate cancer incidence in systemic lupus erythematosus (SLE) as compared to the general population. (2) To estimate the sensitivity and specificity of methods of cancer ascertainment. (3) To determine the prevalence of malignancy risk factors in SLE. Methods. (1) We determined the incidence of malignancy in the Montreal General Hospital (MGH) lupus cohort, through linkage with the Quebec tumor registry. Standardized incidence ratios (SIRS) were generated, using Quebec population rates. In addition, a meta-analysis was performed by pooling data from eight cohort studies of malignancy in SLE. (2) We administered a postal survey to cohort members to determine risk factors for cancer and self-report of cancer occurrence. For dead or lost-to-follow-up patients, data was abstracted from charts. We calculated the sensitivity and specificity of self-report and chart review for cancer ascertainment, compared to registry linkage results. (3) Using the data collected on self-report and chart review, we compared risk factor prevalence within the MGH cohort to that of the Quebec population. Results. (1) Observed cancers in our cohort were greater than what would be expected; for all cancers, the SIR was 1.8 (95% Confidence Interval 1.2--2.6). The meta-analysis SIR (for all malignancies) was 1.67 (1.42--1.94). Postal survey and chart review methods demonstrated high specificity. Sensitivity was imperfect, but did not greatly effect estimation of the SIR estimate. (2) Our lupus cohort had a distinct profile of risk factors for malignancy compared to the general population; differences included more prevalent nulliparity, obesity, and use of hormone replacement therapy. Conclusions. The risk of malignancy in SLE patients is increased. Risk factor profiles could influence the incidence of certain malignancies in SLE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".