Epstein–Barr virus encoded <scp>RNA</scp> detected by <i>in situ</i> hybridization using cytological preparations
Bibliographic record
Abstract
OBJECTIVE: Detection of Epstein-Barr virus (EBV) status might help in the diagnosis of EBV-related neoplasms. The rate of successful assays for the detection of EBV-infected cells in cytological preparations has not been fully explored. Our aims were to examine the rate of successful in situ hybridization (ISH) assays for EBV-encoded RNA (EBER) in cytological specimens and to explore reasons for failure. METHODS: An electronic search selected cases with ISH-EBER assays performed on cytological preparations during a 10-year period. Data regarding patient age, gender and immune status, sample type and site, type of preparation, ISH-EBER results, immunophenotyping and immunohistochemistry results, final diagnosis and correspondent histopathological samples were retrieved. RESULTS: Sixty specimens from 58 patients with diagnoses of lymphoproliferative disorder (n = 35), carcinoma (n = 24) and sarcoma (n = 1) were identified. ISH-EBER assays were performed on 50 cell block sections and on 10 cytospin preparations, with 22 positive and 32 negative results. Six tests (four cytospins and two cell block sections) failed owing to loss of material during the assay and background staining, with an overall failure rate of 10% and 4% if cytospins were excluded. Assays were performed on 13 cytology and surgical specimens from the same site, with only one discrepant result. CONCLUSIONS: Cell block sections had more successful ISH-EBER assays when compared with cytospins. Reasons for failure were loss of material on the slide and background staining. A high concordance rate with surgical specimens emphasizes the usefulness of cytological samples for determining EBV status in patients with exhausted or no histological material available.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".