Letter: Presurgical Identification of Patients With Glioblastoma at Risk for Cognitive Impairment at 3-Month Follow-up
Bibliographic record
Abstract
To the Editor: We commend authors Rijnen et al1 for investigating factors that predispose glioblastoma (GBM) patients to postoperative cognitive impairment. Since then, several studies have investigated cognitive impairment after glioma resection. In a study by Niki et al,2 it was shown that primary cognitive functions correlated to specific brain regions after glioma resection. In left-sided glioma, digit span forward and backward tasks and letter-digit substitution tasks were affected, with associated brain regions, including the posterior middle temporal gyri to the supramarginal gyrus.2 In right-sided glioma, Stroop color-word task and world fluency plus digit span forward tasks were affected, with associated brain regions, including the anterior medial frontal cortex and right inferior frontal cortex, respectively.2 A study investigating the presence of language impairment after resection for insular glioma demonstrated that left-sided insular lesions specifically showed a decline in language performance after surgery, with more critical deficits present when the tumor involved the insula; however, no correlations were discovered between tumor volume, extent of resection, and language impairment.3 It was also established that early surgical treatment in asymptomatic patients with incidental low-grade glioma was associated with either stable or improved neuropsychological outcomes in 87.2% of patients at 3 mo.4 We would like to contribute our suggestions for future implementation in the investigation of postoperative cognition in GBM. To evaluate for cognitive impairment and neuropsychological performance both pre- and postoperatively, metrics can be obtained through the administration of Mini-Mental State Examination, Brief Visuospatial Memory Test-Revised (BVMT-R) and the Rey Auditory Verbal Learning Test (RAVLT).5 Other psychometric instruments used to assess anxiety and depression include but are not limited to the Beck Depression Inventory-II, State-Trait Anxiety Inventory (STAI), and Beck Anxiety Inventory (BAI).6,7 Researchers should be aware that the Montreal Cognitive Assessment (MoCA) may not be a suitable instrument to detect cognitive impairment in patients with newly diagnosed high-grade glioma because of its moderate sensitivity.8 Systemic inflammation was found to be associated with common behavioral symptoms in glioma patients, including depression, anxiety, and cognitive impairment.9 Increased IFN-γ was found to be positively correlated with depression, whereas IL-2 levels were positively correlated with cognitive impairment in glioma patients.9 These levels may be measured both pre- and postoperatively in the future to determine if surgical resection affects systemic inflammation in glioma. The geriatric population is significantly predisposed to experiencing postoperative cognitive dysfunction, and future research may assess the impact of this specific risk after glioma resection.10 Future research should continue to assess structural correlation in regard to the location and hemisphere affected by GBM and its impact on cognitive impairment in the various domains, including language, learning and memory, complex attention, executive function, and motor function. Awake craniotomy preserves language ability and reduces the chances of permanent speech dysfunction in high-grade gliomas,11 whereas for low-grade gliomas, except for psychomotor speed, all cognitive function can be preserved.12 Funding This study did not receive any funding or financial support. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".