Is the difference real, is the difference relevant: the minimal detectable and clinically important changes in the Montreal Cognitive Assessment
Bibliographic record
Abstract
Background and aims: The Montreal Cognitive Assessment (MoCA) is a widely used instrument for assessing cognitive function in stroke survivors. To interpret changes in MoCA scores accurately, it is crucial to consider the minimal detectable change (MDC) and minimal clinically important difference (MCID). The aim was to establish the MDC and MCID of the MoCA within 6 months after stroke. Methods: This cohort study analysed data from the EFFECTS trial. The MoCA was administered at baseline and at 6-month follow-up. The MDC was calculated as the upper limit of the 95 % confidence interval of the standard error of the MoCA mean. The MCID was determined using anchor-based and distribution methods. The visual analogue recovery scale of the Stroke Impact Scale (SIS [primary anchor]) and Euro Quality of Life-5 Dimensions index (EQ-5D [confirmatory anchor]) were used as anchors. The distribution-based method, the Cohen benchmark effect size was chosen. Results: In total, 1131 (mean age [SD], 71 [10.6] years) participants were included. The mean (SD) MoCA scores at admission and 6-month follow-up were 22 (5.2) and 25 (4.2), respectively. The MDC of the MoCA was 5.1 points. The anchor method yielded the MCIDs 2 and 1.6 points for SIS and EQ-5D, respectively. Using the distribution method, the MCID for the MoCA was 1 point. Conclusions: Even a small change in MoCA scores can be important for stroke survivors; however, larger differences are required to ensure that any difference in MoCA values is a true change and is not related to the inherent variation in the test. Due to small sample sizes, the results of the anchor analysis need to be interpreted with caution.
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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 | 0.021 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".