Cytologic identification of serous neoplasms in peritoneal fluids
Bibliographic record
Abstract
BACKGROUND: Recognition of serous neoplasms in peritoneal fluids and their subclassification into serous borderline tumors (SBT) and serous carcinomas (SCA) may be difficult. The objective of this study was to determine whether morphologic criteria can distinguish reactive mesothelial cells (RM), SBT, and SCA (grades 1-3) in peritoneal fluids. METHODS: A blinded review of 42 peritoneal fluids from 40 patients with histologically confirmed RM (n = 10 patients), SBT (n = 7 patients; 3 with primary ovarian tumors and 4 with primary peritoneal tumors), and SCA (n = 23 patients; 7 with grade 1 tumors, 6 with grade 2 tumors, and 10 with grade 3 tumors; 12 with primary ovarian tumors and 11 with primary peritoneal tumors) evaluated papillae presence and size, intercellular windows, group contours, dyshesion, nuclear atypia, size and overlap, cell size, and nucleoli and nuclear-to-cytoplasmic ratios. From these parameters, specimens were classified as RM, SBT versus grade 1 SCA, or grade 2 SCA versus grade 3 SCA. RESULTS: RM were identified as groups of small cells with minimal nuclear atypia and overlap, intercellular windows, and irregular group borders without papillae. No features differentiated SBT from grade 1 SCA. Grade 2 and 3 SCA showed more nuclear atypia and overlap with larger nuclei and cells. Thirty-nine of 42 specimens (95%) were correctly identified as RM or serous neoplasm. Two SBT specimens and one grade 1 SCA specimen were overcalled, and three SCA specimens (two grade 2 and one grade 3) were undergraded. CONCLUSIONS: The distinction of RM from serous neoplasms in peritoneal fluids is possible in most patients by examination of cell group architecture, nuclear atypia, and nuclear size. Differentiation of SBT from grade 1 SCA is not reliable, meriting a differential diagnosis. Most high-grade SCA specimens can be identified by increased nuclear atypia, overlap, and size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".