Intraoperative Flow Cytometry for the Evaluation of Meningioma Grade
Bibliographic record
Abstract
Meningiomas are the most frequent central nervous system tumors in adults. The majority of these tumors are benign. Nevertheless, the intraoperative identification of meningioma grade is important for modifying surgical strategy in order to reduce postoperative complications. Here, we set out to investigate the role of intraoperative flow cytometry for the differentiation of low-grade (grade 1) from high-grade (grade 2-3) meningiomas. The study included 59 patients. Intraoperative flow cytometry analysis was performed using the 'Ioannina Protocol' which evaluates the G0/G1 phase, S-phase, mitosis and tumor index (S + mitosis phase fraction) of a tumor sample. The results are available within 5 min of sample receipt. There were 41 grade 1, 15 grade 2 and 3 grade 3 meningiomas. High-grade meningiomas had significantly higher S-phase fraction, mitosis fraction and tumor index compared to low-grade meningiomas. High-grade meningiomas had significantly lower G0/G1 phase fraction compared to low-grade meningiomas. Thirty-eight tumors were diploids and twenty-one were aneuploids. No significant difference was found between ploidy status and meningioma grade. ROC analysis indicated 11.4% of tumor index as the optimal cutoff value thresholding the discrimination between low- and high-grade meningiomas with 90.2% sensitivity and 72.2% specificity. In conclusion, intraoperative flow cytometry permits the detection of high-grade meningiomas within 5 min. Thus, surgeons may modify tumor removal strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".