Exploring the measurement properties of the Montreal Cognitive Assessment in a population of people with cancer G
Bibliographic record
Abstract
Background Cancer and cancer-related treatments are associated with a constellation of physical and psychological changes.Treatments associated with noncentral nervous system neoplasms can have short-and long-term effects on cognition, affecting quality of life in people with cancer.Clinical measurement tools specific to cancer-related mild cognitive impairment (MCI) are lacking.The Montreal Cognitive Assessment (MoCA) has been validated in a geriatric population and used in studies assessing MCI in persons with cancer, but no studies have yet shown its psychometric properties when used with this population.Purpose The purpose of this study is to explore the psychometric properties of the MoCA within a population of persons with noncentral nervous system cancer.Methods A total of 74 participants were included from persons attending a Cancer Nutrition-Rehabilitation Program at the McGill University Health Centre.Rasch analyses were conducted. ResultsThe MoCA data fit all the properties of the Rasch model with a person separation index of 1.04 and person reliability of 0.52.The MoCA items were found to measure a unidimensional construct and spanned 6.57 logits, with item difficulty levels between 2.49 and -4.08 logits.However, the MoCA presented a lack of items of higher difficulty, as person cognitive ability levels ranged from -0.51 to 5.17 logits.Conclusion Within the limits of a small sample size, the results of this exploratory study suggest the possibility that the MoCA, when used within a population of persons with cancer, may meet criteria for unidimensionality and adequate item fit but may present weaknesses when used with participants of higher cognitive abilities.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".