A Clinical Prediction Rule for Lymphoma Development in Primary Sjögren’s Syndrome
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a practical prediction rule for the progression from primary Sjögren's syndrome (pSS) to B cell non-Hodgkin's lymphoma (B cell NHL) based on the combination of routinely available clinical and serological disease variables. METHODS: The case records of 563 patients with pSS were reviewed, and their demographic, clinical, and immunologic features were collected. Multivariate logistic regression analysis was performed to identify independent risk factors for lymphoma development and to create a propensity score for discrimination between patients at risk of B cell NHL and those patients not at risk. The model was internally validated by resampling procedures. RESULTS: Out of 563 patients with pSS, 387 fulfilling the American European Consensus Group criteria (12 with B cell NHL, 375 without B cell NHL) were included in our study. Salivary gland enlargement (p = 0.001), low C3 (p = 0.035) and/or C4 levels (p = 0.021), and disease duration (p = 0.001) were identified as independent risk factors for B cell NHL in pSS. The optimal threshold of the propensity score was determined at Y = 4.26, which allowed us to identify patients who develop B cell NHL with a sensitivity of 78% and specificity of 95%. The leave-one-out cross-validated prediction error was 6%, and the median bootstrapped sensitivity and specificity were 71% and 95%, respectively. CONCLUSION: We created a "bedside" prediction model for the identification of patients with pSS who are at risk for B cell NHL, which revealed an excellent discriminative ability and a good internal and external reproducibility.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".