Cerebrospinal fluid flow cytometry: utility in central nervous system lymphoma diagnosis
Bibliographic record
Abstract
Flow cytometry of the cerebrospinal fluid (CSF) is used in isolation or as an adjunct to cytology to increase the sensitivity of detecting primary central nervous system (CNS) lymphoma. We aim to evaluate the sensitivity of CSF flow cytometry as a diagnostic tool for primary CNS lymphoma in patients presenting with undifferentiated neurologic symptoms. We retrospectively reviewed all CSF samples received by the Calgary Laboratory Services Flow Cytometry Laboratory from 2012- 2015. Clinical data, laboratory investigations, radiologic imaging studies, and pathological data were analyzed. Clinical review extended to 2 years post CSF flow cytometric testing. The number of samples of CSF flow cytometry that were positive for a hematological malignancy was 43/763 (5.6%). The overall sensitivity of the test was 69.4%. A positive result was more likely to occur in patients with a prior history of a hematological malignancy or abnormal enhancement on MRI (p<0.0001). CSF flow cytometry was negative in all patients who did not have a previous hematological malignancy or abnormal enhancement on MRI (n = 247). CSF flow cytometry has a limited role in screening for primary CNS lymphoma, unless a strictly endorsed testing algorithm is applied. It is, however, an invaluable tool in evaluating CNS involvement in patients with a previous diagnosis of hematolymphoid malignancy or abnormal enhancement on MRI. LEARNING OBJECTIVES This presentation will enable the learner to: Discuss the costs and benefits of using CSF flow cytometry to diagnose CNS lymphoma 1. Identify appropriate clinical indications for using CSF flow cytometry as a first-line test 2. Apply a testing algorithm to increase the diagnostic yield of CSF flow cytometry
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".