Cognitive Screening in Brain Tumors: Short but Sensitive Enough?
Bibliographic record
Abstract
Cognitive deficits in brain tumors are generally thought to be relatively mild and non-specific, although recent evidence challenges this notion. One possibility is that cognitive screening tools are being used to assess cognitive functions but their sensitivity to detect cognitive impairment may be limited. For improved sensitivity to recognize mild and/or focal cognitive deficits in brain tumors, neuropsychological evaluation tailored to detect specific impairments has been thought crucial. This study investigates the sensitivity of a cognitive screening tool, the Montreal Cognitive Assessment (MoCA), compared to a brief but tailored cognitive assessment (CA) for identifying cognitive deficits in an unselected primary brain tumor sample (i.e., low/high-grade gliomas, meningiomas). Performance is compared on broad measures of impairment: (a) number of patients impaired on the global screening measure or in any cognitive domain; and (b) number of cognitive domains impaired and specific analyses of MoCA-Intact and MoCA-Impaired patients on specific cognitive tests. The MoCA-Impaired group obtained lower naming and word fluency scores than the MoCA-Intact group, but otherwise performed comparably on cognitive tests. Overall, based on our results from patients with brain tumor, the MoCA has extremely poor sensitivity for detecting cognitive impairments and a brief but tailored CA is necessary. These findings will be discussed in relation to broader issues for clinical management and planning, as well as specific considerations for neuropsychological assessment of brain tumor patients.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".