Bibliographic record
Abstract
OBJECTIVES: To determine the incidence and clinical and cytologic diagnostic accuracy of vitreoretinal lymphoma (VRL) and to evaluate its clinical features, management, and outcomes in a cohort of patients who underwent diagnostic vitrectomy. METHODS: Retrospective medical record review of 463 diagnostic vitrectomy specimens from 430 patients collected from October 1, 1990, through December 31, 2010, from Vancouver General Hospital and the British Columbia Cancer Agency. RESULTS: A total of 22 patients were diagnosed as having VRL with a preoperative clinical diagnostic sensitivity of 77%, specificity of 73%, positive predictive value of 13%, and negative predictive value of 98%. The cytologic diagnostic sensitivity was 87% (27 of 31 specimens). The incidence of VRL in British Columbia doubled from 1990 to 2010, with a final incidence of 0.047 cases per 100 000 people per year. The mean age at diagnosis was 66 years. Seventeen patients (77%) were women. The initial diagnosis of lymphoma was VRL in 19 patients (86%), of whom 7 (37%) had concurrent central nervous system lymphoma. Recurrent disease was found in 11 patients. Large B-cell lymphoma was diagnosed in 20 patients (91%). The median progression-free survival was 11 months, and the median survival was 33 months from the initial diagnosis. CONCLUSIONS: Vitreoretinal lymphoma remains a clinical diagnostic challenge. Early clinical suspicion with subsequent diagnostic vitrectomy for cytologic analysis and collaboration with the oncology department is critical to appropriate and prompt staging and treatment. More interdisciplinary studies are required to further characterize VRL and maximize the therapeutic options, thus improving the morbidity and mortality associated with the disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".