Rose Bengal Staining of the Temporal Conjunctiva Differentiates Sjögren's Syndrome from Keratoconjunctivitis Sicca
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 ≤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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".