B-cell and T-cell quantification in minor salivary glands in primary Sjögren’s syndrome: development and validation of a pixel-based digital procedure
Bibliographic record
Abstract
BACKGROUND: Evaluating lymphocytic infiltration of minor salivary gland biopsy in primary Sjögren's syndrome is challenging. We developed and evaluated a digital method for quantifying B and T lymphocytes in whole minor salivary gland biopsy slides. METHODS: Minor salivary gland biopsies were immunostained with anti-CD20/anti-CD3 antibodies using red/brown chromogens. Slides were digitised and spliced into mosaics of smaller JPEG format images in which red and brown pixels were counted. ImageJ Cell counter was used for validation. Agreement between the digital and manual methods was evaluated using Bland-Altman plots and the interclass correlation coefficient. External validation relied on the Chisholm-Mason, Tarpley, and focus-score methods. RESULTS: Of 62 minor salivary gland biopsy slides, 61.3 % had a Chisholm-Mason grade ≥ III or a focus score ≥1. The number of pixels correlated well with manual cell counts (r = 0.95 for red pixels vs. B cell count and r = 0.91 for brown pixels vs. T cell count). Interclass correlation coefficients between digital and manual counts were excellent (0.92 for B/T cells). B-cell proportion showed a significant positive correlation with the focus score (Spearman's coefficient 0.463, p < 0.0001). Median B-cell proportion was lower in minor salivary gland biopsies with Chisholm grades I-II (2.5 % (0.2-13.9)) than III-IV (30.0 % (15.5-45.2)) and increased with Tarpley's class (1, 2.2 % (0.2-6.6); 2, 27.2 % (13.0-38.9); and 3-4, 48.5 % (29.4-56.4); p < 0.001 for all comparisons). Minor salivary gland biopsy B-cell proportion was also significantly correlated with several markers of clinical and biological activity of the disease, especially with markers of systemic B-cell hyperactivation. CONCLUSION: The digital procedure proved accurate compared to the reference standard, producing reliable results for whole tissue sections. TRIAL REGISTRATION: ClinicalTrials.gov [ NCT00740948 ]. Registered 22 August 2008.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".