Are Salivary Gland Ultrasonography Scores Associated with Salivary Flow Rates and Oral Health-related Quality of Life in Sjögren Syndrome?
Bibliographic record
Abstract
Objective. Major salivary gland ultrasonography (SGUS) is a widely used imaging technique to evaluate salivary gland involvement in primary Sjögren syndrome (pSS). The aim of this study was to evaluate the relationship between SGUS, salivary flow rate (SFR) as an objective measure of the gland function, and oral health-related quality of life (OHRQOL) as a patient-reported outcome measure (PROM) in a pSS cohort. Methods. Sixty-six patients with pSS were examined by SGUS according to Hocevar and Milic scoring systems. Patients with inhomogeneity/hypoechoic areas with scores ≥ 2 in parotid and submandibular glands were classified separately as “severe glandular involvement.” Further, oral health, SFR, and Oral Health Impact Profile-14 (OHIP-14) for OHRQOL were assessed. Results Both total Hocevar and Milic scores were higher in 21 pSS patients with low unstimulated whole salivary flow rate (U-WSFR) than 45 pSS patients without low U-WSFR ( P = 0.001 and P < 0.0001, respectively). Increased scores of homogeneity, hypoechoic areas and glandular border visibility were observed in patients with low U-WSFR ( P < 0.05). Among these variables, homogeneity score was found to be an independent risk factor for low U-WSFR in pSS according to logistic regression analysis (OR 1.586, P = 0.001). Moreover, a higher OHIP-14 score was observed in severe parotid involvement compared to nonsevere cases (23.26 ± 21.19 vs 8.32 ± 13.82, P = 0.004). Conclusion. High Milic and Hocevar SGUS scores are associated with reduced SFR and poor OHRQOL as a PROM. The inhomogeneity component of the SGUS score is associated with low U-WSFR and is an indicator of severely affected gland function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".