Bibliographic record
Abstract
Abstract PURPOSE To evaluate the diagnostic accuracy of preoperative MRS in the determination of the IDH status and grade in patients with newly diagnosed gliomas. METHODS MRS was performed using a regular PRESS sequence and a spectral editing sequence (MEGA-PRESS). Concentration of 2-HG was estimated from the subtracted spectra from the edited MRS sequence while NAA, choline and creatinine concentrations were obtained from the regular PRESS sequence. IDH mutation status was assessed by immunohistochemistry for all patients and additional next generation sequencing for all grade 2 and 3 gliomas. Differences in metabolite concentrations between IHD-mutant and wild-type gliomas and between gliomas of various grades were assessed using non parametric tests. TAreas under-the ROC curve (AUC) for different metabolites were calculated with IDH mutation status or glioma grade as the outcome. RESULTS There were 29 IDH-mutant gliomas and 52 wild-type gliomas. There was a significant difference in the NAA/Choline ratio among various glioma grades (P<0.05). The AUC for 2HG was 0.74 in the differentiation between IDH-mutant vs wild type gliomas. Using a 2-HG cut-off of >0.96 I.U., sensitivity was 59% and specificity was 90% for the identification of IDH-mutant gliomas. The AUC for the NAA/Choline ratio was 0.74 in the differentiation of high vs low grade gliomas. Using a NAA/Choline cut-off ≤0.53, sensitivity was 45% and specificity was 100% for identification of high grade gliomas. CONCLUSION Preoperative MRS can identify IDH-mutant gliomas and high grade gliomas with high specificity.
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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".