A Stratification Approach Based on Salivary Gland Ultrasonography for Assessing Secretory Function in Sjögren Disease
Bibliographic record
Abstract
Objective Our aim was to develop an ultrasonographic scoring model for staging hypofunction of salivary glands (SGs) in patients with Sjögren disease (SjD). Methods The assessment of SG secretory hypofunction was conducted by measuring whole salivary flows. B-mode ultrasonography was performed bilaterally on the parotid and submandibular glands to quantitatively evaluate the gland score and Outcome Measures in Rheumatology (OMERACT) score. The correlation between these scores and SG secretory function in patients with SjD was analyzed, leading to the development of an ultrasonographic scoring model for staging SG hypofunction. Results A 1-center derivation cohort comprising 164 patients with SjD and a double-center validation cohort consisting of 107 patients with SjD were included. Both ultrasonographic scores demonstrated excellent discriminatory ability between patients with SjD with hypofunction and those with normal function (area under the curve > 0.8 for both; P < 0.001). A novel ultrasonographic scoring model revealed that low total OMERACT scores (< 5) indicated initial-stage SG hypofunction, whereas high scores (> 9) suggested end-stage hypofunction. Conversely, patients with moderate-level total OMERACT scores (5-9) required further stratification using total gland scores. The incidence of SG hypofunction among all 271 patients with SjD was found to be 18% in the initial stage, 58% in the progressive stage, and 100% in the end stage ( P < 0.01). Further, the incidence of lacrimal gland involvement and hyperglobulinemia (IgG > 16 IU/mL) was significantly lower in the initial-stage patients compared to those at other stages (all P < 0.01). Conclusion The novel ultrasonographic scoring model incorporates precise definitions for each stage of SG hypofunction, providing a robust and clinically significant approach to stratification of SG secretory hypofunction in SjD.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".