Diagnostic Value of Salivary Gland Ultrasonographic Scoring System in Primary Sjögren’s Syndrome: A Comparison with Scintigraphy and Biopsy
Bibliographic record
Abstract
OBJECTIVE: To compare an ultrasonographic (US) scoring system of salivary glands with scintigraphy and salivary gland biopsy, in order to evaluate its diagnostic value in primary Sjögren's syndrome (SS). METHODS: In 135 patients with suspected SS, the grades of 5 US measures of both parotid and submandibular salivary glands were scored (0-48 scale). Diagnosis of primary SS was established following the American-European Consensus Group criteria of 2002. The patients' total scintigraphic score (0-12 scale) was determined and the histopathological changes of minor salivary glands graded. Area under the receiver-operating characteristic (ROC) curve was employed to evaluate the diagnostic value of the US scoring system. RESULTS: Primary SS was diagnosed in 107 (79.2%) patients and the remaining 28 subjects (20.8%) constituted the control group. US changes of salivary glands were established in 98/107 patients with SS and in 14/28 controls. Mean US score was 26 in SS patients and 6 in controls. Through ROC curves, US arose as the best performer (0.95 +/- 0.01), followed by scintigraphy (0.86 +/- 0.31). Setting the cutoff score for US at 19 resulted in the best ratio of specificity (90.8%) to sensitivity (87.1%), while setting the cutoff scintigraphic score at 6 resulted in specificity of 86.1% and sensitivity of 67.1%. Among 70 patients with US score >or= 19, a scintigraphic score > 6 was recorded in 54/70 (77.1%) and positive biopsy findings in 62/70 (88.5%) patients. CONCLUSION: We show high diagnostic accuracy of a novel US scoring system of salivary glands (0-48) in patients with primary SS comparable to invasive methods, i.e., scintigraphy and salivary gland biopsy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".