Comparison of Conventional Cytology Vs. High Sensitivity Flow Cytometry for the Diagnosis of Leptomeningeal Involvement by Hematological Lymphoid Malignancies
Bibliographic record
Abstract
Abstract Abstract 3112 Objective: To compare the utility of high sensitivity (5-colour) flow cytometry (FCM) vs. conventional cytology (CC) for detecting cerebrospinal fluid (CSF) involvement in patients with hematological lymphoid malignancies. Methods: The results of diagnostic evaluations on all CSF samples analyzed for involvement by neoplastic lymphoid cells between January 2005 and February 2010 were reviewed retrospectively. Cases were identified by reviewing logs of all FCM procedures performed during that time period. FCM was performed on the CSF using a 5-antibody panel (“high sensitivity”). Result: 108 patients (62M/46F) diagnosed with non-Hodgkin lymphoma or acute lymphoblastic leukemia underwent a total of 609 lumbar punctures (LP). The 359 samples that were sent for both CC and FCM form the basis of this analysis. The majority of the LP's (312/359, 87%) were negative for malignant cells by both FCM and CC (FCM-/CC-). 47 samples showed infiltration by tumor cells; of these, 25 (7%) were FCM+/CC+, and 22 (6%) were FCM+/CC-. No cases (0%) were FCM-/CC+. Using FCM as the gold standard, CC had a specificity of 100%, but a sensitivity of only 53%. Conclusion: High-sensitivity FCM has superior sensitivity to CC for diagnosing leptomeningeal involvement by lymphoid malignancies. CC failed to identify any additional cases that were not seen on FCM. This raises the question of whether performing CC on CSF samples to search for neoplastic lymphoid cells is of any additional diagnostic value. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".