Continuous Real-Time Neuropsychological Testing during Resection Phase in Left and Right Prefrontal Brain Tumors
Bibliographic record
Abstract
BACKGROUND: Executive functions are multi-component and are based on large-scale brain networks. For patients undergoing brain surgery in the prefrontal cortex, resection in the anterior prefrontal sites is assisted by continuous monitoring of their performance on several tasks measuring components of executive functions. In this study, we did not test patients during direct cortical stimulation, but during resection itself. We chose tests routinely used to assess executive functions and included them in a protocol for left (LH) and right (RH) hemisphere prefrontal resections. This protocol is meant to be used during real-time neuropsychological testing (RTNT)-an already established monitoring technique. METHODS: We retrospectively reviewed a consecutive series of 29 adult patients with glioma in the superior and middle frontal areas who performed the RTNT sequence throughout the resection phase. The testing protocol comprised 10 tests for LH frontal resections and 9 tests for RH frontal resections. RESULTS: > 0.05, n.s.). Mean extent of resection was 95.33% ± 9.72 for the RH group and 94.64% ± 6.74 for the LH group. Patients showed good performance post- vs. pre-surgery. The greater difference in the number of LH patients scoring within the normal range was found for the symbol-digit modality test (83.3% to 62%), Stroop test (100% to 77%) and short-term memory (84.61% to 72.72%) and working memory (92.3% to 63.63%). For RH patients, the main changes were observed on the clock drawing test (100% to 77.7%) and cognitive estimation (100% to 72.7%). CONCLUSIONS: Frontal RTNT offers continuous and reliable feedback on the patients' cognitive status during resection in frontal areas.
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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 | 0.001 | 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) | 0.001 | 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".