Clinical Significance of Cytocentrifuge Preparation Analysis of Low-Cell-Count Cerebrospinal Fluid Specimens.
Bibliographic record
Abstract
Abstract Introduction: The usefulness of performing a cytocentrifuge preparation (CCP) analysis on cerebrospinal fluid (CSF) containing less than 5 cells has never been studied. To determine if the CCP analysis has a clinical impact on central nervous system (CNS) management of patients with CSF samples containing less than five cells, we correlated the result of CCP to cytomorphology and flow cytometry. Methods: From November 20th 2005 to July 18th 2007, CCP analysis were performed on 105 CSF consecutive samples containing less than 5 cells from patients with various cancers. For each of these samples, a research via computerized data was conducted to obtain cytomorphology and flow cytometry results. If results were positive or inconclusive, the clinical history and treatment were reviewed. Results: All 105 samples had negative CCP results. Of the 105 samples, 74 (70,5%) had available cytomorphologies, with 5 (6,8%) being positive, confirming central nervous system infiltration. All 5 patients received treatment for their CNS involvement. The sensitivity of cytomorphology was 83,3%(5/6) and its specificity 100% (68/68).Of the 105 samples, 23 (21,9%) had available flow cytometries: 4 of the 23 (17,4%) cytometries were positive, 2 correlated by positive cytomorphology and treated. 2 had negative cytomorphology with one patient receiving treatment. The sensitivity of cytometry was 75% (3/4) and the specificity 94,7% (19/20). Conclusion: We found no advantage to performing CCP analysis on CSF samples of less than 5 cells. However, this suggests that performing both cytomorphology and flow cytometry allows a better detection and management of CNS infiltration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".