Comprehensive Laboratory Diagnostic Workup for Patients with Suspected Intraocular Lymphoma including Flow Cytometry, Molecular Genetics and Cytopathology
Bibliographic record
Abstract
Background: Intraocular lymphoma (IOL) presents a real challenge in daily diagnostics. Cyto- and/or histopathology of vitreous body represent the diagnostic cornerstones. Yet, false negative results remain common. Therefore, we analyzed the diagnostic significance of flow cytometry (FC) within the workup algorithm of IOL and compared its sensitivity with the results obtained from routine cytopathology and molecular genetics; Methods: Seven patients undergoing vitrectomy due to suspected IOL were investigated by FC and parallel cytopathology and, if available, digital droplet PCR (ddPCR) for MYD88 L265P; Results: Four out of seven patients were finally diagnosed with IOL. Among the IOL patients, cytopathology confirmed the presence of lymphoma cells in only two cases. In contrast, FC was positive for IOL in all four cases, and FC additionally confirmed the lack of IOL in the remaining patients. In IOL patients diagnosed by FC and with available ddPCR, the diagnosis of IOL was confirmed by the presence of the MYD88 L265P mutation in all three patients; Conclusions: The combination with FC was superior to cytopathology alone in the diagnostic work-up of IOL, and it showed an excellent correlation with ddPCR results. A comprehensive diagnostic panel consisting of cytopathology, FC and molecular genetics should be considered for the work-up of suspected IOL.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".