Evaluation of cognitive function in patients with brain tumors: mini-mental state examination verse Montreal cognitive assessment scale
Bibliographic record
Abstract
Objective To compare the value of mini-mental state examination (MMSE) to assessment of cognitive function in patients with brain tumors with that of Montreal cognitive assessment scale (MoCA). Methods Three hundred and two patients with brain tumors (tumor group) and two hundred normal relatives of the patients with brain tumors (control group) were recruited in this study. The examinations of MMSE and MoCA were performed in the tumor group and control group. Then the scores of MMSE and MoCA were analyzed statistically. Results Both overall scores of MMSE and MoCA were significantly lower in tumor group than those in control group (P 0.01). According to comprehensive evaluation including chief complains, MMSE, MoCA, comparison to control group and physician assessment, the incidence of cognitive impairment was 45.1% (139/308) in tumor group. According to MoCA, 129 patients in tumor group had cognitive impairment, of whom 114 had cognitive impairment according to comprehensive evaluation and 15 not. The sensitivity and specificity of MoCA were 82.0% and 92.7% respectively. According to MMSE, 27 of 139 patients who had cognitive impairment in tumor group according to comprehensive evaluation, had cognitive impairment. The sensitivity and specificity of MMSE were 19.4% and 100% respectively. Conclusions The MoCA is more sensitive than the MMSE to screening cognitive impairment in patients with brain tumors although they have similar specificity. The setting of MoCA sub-items is more reasonable and suitable to evaluating cognitive function in patients with brain tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".