Neurocognitive Function in Newly Diagnosed Low-grade Glioma Patients Undergoing Surgical Resection With Awake Mapping Techniques
Bibliographic record
Abstract
BACKGROUND: Low-grade glioma (LGG) patients have increased life expectancy, so interest is high in the treatments that maximize cognition and quality of life. OBJECTIVE: To examine presurgical baseline cognitive deficits in a case series of LGG patients and determine cognitive effects of surgical resection with awake mapping. METHODS: We retrospectively assessed neurological deficits, subjective concerns from patient or caregiver, and cognitive deficits at baseline and postsurgery for 22 patients with newly diagnosed LGG who underwent baseline neuropsychological evaluation and magnetic resonance imaging before awake surgical resection with mapping. Twelve of the 22 patients returned for postoperative evaluation approximately 7 months after surgery. RESULTS: At baseline, 92% of patients/caregivers reported changes in cognition or mood. Neurological examinations and Montreal Cognitive Assessment Scale scores were largely normal; however, on many tests of memory and language, nearly half of individuals showed deficits. After surgery, 45% had no deficits on neurological examination, whereas 55% had only transient or mild difficulties. Follow-up neuropsychological testing found most performances stable to improved, particularly in language, although some patients showed declines on memory tasks. CONCLUSION: Most LGG patients in this series presented with normal neurological examinations and cognitive screening, but showed subjective cognitive and mood concerns and cognitive decline on neuropsychological testing, suggesting the importance of comprehensive evaluation. After awake mapping, language tended to be preserved, but memory demonstrated decline in some patients. These results highlight the importance of establishing a cognitive baseline before surgical resection and further suggest that awake mapping techniques provide reasonable language outcomes in individuals with LGG in eloquent regions.
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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 | 0.001 | 0.001 |
| 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".