Rose Bengal Staining of the Temporal Conjunctiva Differentiates Sjögren's Syndrome from Keratoconjunctivitis Sicca
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To compare the clinical presentation of 231 patients with primary Sjögren's syndrome (pSS) with 89 patients with aqueous-deficient dry eye (keratoconjunctivitis sicca; KCS), to determine those procedures that best differentiate these groups in the eye care clinic. METHODS: The records of all patients seen at the University Health Network Sjögren's Syndrome Clinic from October 1992 to July 2006 were reviewed and documented. The diagnosis of pSS was based on the AECC (American European Consensus Criteria) of 2002. KCS control subjects were non-SS patients with symptoms of dry eye and Schirmer scores of <or=10 mm in 5 minutes in at least one eye. There were 90 variables used in the analysis of the total database. Recursive partitioning was used to generate tree diagrams that demonstrated which characteristics best distinguished pSS from KCS. RESULTS: Recursive partitioning of the full database demonstrated that the serum immunoglobulin Ro and the status of the salivary gland biopsy were most important in distinguishing pSS and KCS. The presence of rose bengal staining of the temporal conjunctiva was the most important noninvasive ocular variable that separated the groups. Total rose bengal staining also improved sensitivity. When only noninvasive techniques were used, staining of the temporal conjunctiva and severity of dry mouth symptoms were the major factors in distinguishing pSS from KCS. CONCLUSIONS: Rose bengal staining of the ocular surface is an important observation in the detection of SS and the differentiation of pSS and KCS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it