Canadian Normative Data for Minimal Assessment of Cognitive Function in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus-based collection of neuropsychological tests that evaluate cognitive functioning in individuals with multiple sclerosis (MS). The tests are typically scored using each respective published test manual, leaving the examiner to make interpretations from norms derived from different American populations. Given demographic differences, this may lead to misinterpretation of findings in Canadians. Our goal was to establish both discrete and regression-based normative data for the MACFIMS based on a largely co-normed Canadian population to allow for improved psychometric interpretation. METHODS: MACFIMS data sets were aggregated from across three different Canadian cities (Ottawa, Toronto, and London), yielding a total of 330 healthy control participants from four different studies evaluating cognition in individuals with MS. Given the variety of contributing studies, there was variability in terms of the number of participants completing each measure. RESULTS: Both age-based discrete normative data and demographically adjusted (sex, age, and education) regression-based formulae were established. The demographic variables varied in their contribution to each MACFIMS test in the regression models, predicting 0 to 18% of the variance. CONCLUSIONS: Provision of these regression-based formulae will allow for more accurate interpretation of Canadian-derived MACFIMS scores by allowing clinicians to correct for all relevant demographic variables simultaneously, leading to improved clinical decision making for individuals with multiple sclerosis.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".