Neurocognitive impairment, neurobehavioral symptoms, fatigue, sleep disturbance, and depressive symptoms in patients with newly diagnosed glioblastoma
Bibliographic record
Abstract
Background: In addition to poor survival rates, individuals with glioblastoma (GBM) are at risk of neurocognitive impairment due to multiple factors. This study aimed to characterize neurocognitive impairment, neurobehavioral symptoms, fatigue, sleep disturbance, and depressive symptoms in newly diagnosed GBM patients; and to examine whether neurobehavioral symptoms, fatigue, sleep, and depressive symptoms influence neurocognitive performance. Methods: This study was part of a prospective, inception cohort, single-arm exercise intervention in which GBM patients underwent a neuropsychological assessment shortly after diagnosis (median 4 weeks; ie, baseline) and 3, 6, 12, and 18 months later, or until tumor progression. Here, we present baseline data. Forty-five GBM patients (mean age = 55 years) completed objective neurocognitive tests, and self-report measures of neurobehavioral symptoms, fatigue, sleep disturbance, and depressive symptoms. Results: Compared to normative samples, GBM patients scored significantly lower on all neurocognitive tests, with 34 (76%) patients exhibiting neurocognitive impairment. Specifically, 53% exhibited impairment in memory retention, 51% in executive function, 42% in immediate recall, 41% in verbal fluency, and 24% in attention. There were high rates of clinically elevated sleep disturbance (70%), fatigue (57%), depressive symptoms (16%), and neurobehavioral symptoms (27%). A multivariate regression analysis revealed that depressive symptoms are significantly associated with neurocognitive impairment. Conclusions: GBM patients are vulnerable to adverse outcomes including neurocognitive impairment, neurobehavioral symptoms, fatigue, sleep disturbance, and depressive symptoms shortly after diagnosis, prior to completing chemoradiation. Those with increased depressive symptoms are more likely to demonstrate neurocognitive impairment, highlighting the need for early identification and treatment of depression in this population.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".