The relationship between cognition and functional outcomes in rehabilitation: FIMCog vs. MoCA
Bibliographic record
Abstract
AIM: To understand the relationship between scores on two standardized measures of cognition, the Montreal Cognitive Assessment (MoCA) and the cognitive subscale of the Functional Independence Measure (FIMCog), and whether these scores can predict functional outcomes in rehabilitation. METHODS: Retrospective data analysis was conducted on all inpatients admitted to a general rehabilitation unit within a 6-month period (N = 477). The average age of patients was 74 years. The Functional Independence Measure (FIM) was completed for all patients on admission and discharge. The MoCA was administered to patients on clinical suspicion of cognitive impairment. The MoCA was completed with 116 patients. Cognitive status was assessed using FIMCog and MoCA. The motor subscale of FIM was used to assess functional status in calculating the motor Rehabilitation Functional Gain (mRFG) and motor Rehabilitation Functional Efficiency (mRFE) scores. Discharge destination was also used as an outcome measure. RESULTS: There was a moderate correlation between FIMCog and MoCA scores on admission (r = 0.49, P < 0.001). Higher FIMCog and MoCA scores were associated with higher mRFG and mRFE scores. There was an indication that patients with higher MoCA scores were more likely to be discharged to a private residence (adjusted odds ratio 1.11; 95% confidence interval: 0.99, 1.25, P = 0.072). Cut-off points of <25 on the MoCA (sensitivity 88.9%, specificity 48.9%), and <29 on the FIMCog (sensitivity 77.8%, specificity 53.3%) predicted those patients who were less likely to discharge to a private residence. CONCLUSIONS: FIMCog and MoCA scores on admission were moderately correlated, and strongly correlated with functional rehabilitation outcomes. The FIMCog and MoCA had moderately high utility in predicting discharge destination. Geriatr Gerontol Int 2020; 20: 336-342.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".