Prospective comparison of the Montreal Cognitive Assessment (MoCA) and the Mini Mental State Examination (MMSE) in geriatric oncology
Bibliographic record
Abstract
INTRODUCTION: Among the various instruments recommended by the International Society of Geriatric Oncology, the Mini Mental State Examination (MMSE) is the most commonly used cognitive screening test before oncological treatment. Although the Montreal Cognitive Assessment (MoCA) has been shown to be more sensitive than the MMSE in several pathologies, no specific data exist for older patients with cancer. We aimed to compare the proportions of older patients with cancer who had screened positive for cognitive impairment according to the MMSE and MoCA scores obtained during a pretherapeutic geriatric assessment (GA) in oncology. PATIENTS AND METHODS: This prospective study was conducted among 66 patients older than 70 years who were candidates for a first-line treatment for either a solid tumor or hematological malignancy. Patients with brain tumor or previously known dementia were ineligible. During GA, cognitive function was assessed using both the MoCA and the MMSE tests administered in a random order. RESULTS: Median age was 78 years. Most patients (n = 43, 65.2%) had a solid tumor. The median scores were 26 [11-30] for MMSE and 24 [8-30] for MoCA. Thirteen (19.7%) and 44 (66.7%) patients were screened positive for cognitive impairment according to MMSE and MoCA scores, respectively. Overall, 55 (66.7%) patients were screened positive: 12 (21.8%) on both scores, 32 (70.5%) with the MoCA only, and one patient with MMSE only (p < 0.0001). CONCLUSION: The MoCA test seems to be most relevant to screen for cognitive impairment in older patients with cancer.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".