Performance Characteristics of Cerebrospinal Fluid Cytology: An Analysis of Responses From the College of American Pathologists Nongynecologic Cytopathology Education Program
Bibliographic record
Abstract
CONTEXT: - Cerebrospinal fluid cytology is a critical diagnostic tool for the diagnosis of many conditions affecting the central nervous system. OBJECTIVE: - To assess the performance characteristics of cerebrospinal fluid cytology samples by evaluating participant interpretations within the College of American Pathologists Nongynecologic Cytopathology Education program. DESIGN: - Participant interpretations (N = 46 264) evaluated in the College of American Pathologists Nongynecologic Cytopathology Education Program were examined for concordance with the general category and with the reference diagnosis. Two nonlinear mixed models were used to analyze the concordance rates. RESULTS: - The overall concordance rates for the general category and reference diagnosis were 92.1% and 81.0%, respectively. In the malignant category, the concordance rates with the reference diagnosis were lowest for diagnoses of nonhematopoietic small blue round cell tumors (54.8%) and metastatic malignancy (77.5%); the concordance rate with the reference diagnosis was highest for leukemia/lymphoma (94.0%). In the benign category, the concordance rate was lowest for normal cerebrospinal fluid reference diagnoses (58.6%), followed by acute and chronic inflammation (64.6%), fungal infection (80.8%), and macrophages (85.3%). Significant differences in concordance were uncovered when performance was evaluated by participant type and stain technique. Leukemia/lymphoma was the most common diagnosis for misclassified nonhematopoietic small blue round cell tumor cases and negative or inflammatory cerebrospinal fluid cases. CONCLUSIONS: - This study illustrates the difficulties in achieving accurate diagnoses from cerebrospinal fluid specimens, particularly for nonhematopoietic small blue round cell tumors and normal and inflammatory cerebrospinal fluid specimens.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".