Test-retest reliable coefficients and 5-year change scores for the MMSE and 3MS
Bibliographic record
Abstract
The present study explored several different procedures for determining the amount of change that occurred on the Mini-Mental State Exam [MMSE; Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). "Mini-Mental State": A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189-198] and Modified Mini-Mental State Exam [3MS; Teng, E. L., & Chui, H. C. (1987). The Modified Mini-Mental State (3MS) examination. Journal of Clinical Psychiatry, 48, 314-318] over short and extended test-retest intervals. The test-retest scores were drawn from a selected sample of elderly individuals who participated in the Canadian Study of Health and Aging [Canadian Study of Health and Aging. (1994). The Canadian study of health and aging: Study methods and prevalence of dementia. Canadian Medical Association Journal, 150, 899-913] and were tested on two occasions (CSHA-1 and CSHA-2) separated by 5 years. On each occasion the MMSE and 3MS were administered twice at approximately 3-month intervals. Thus, the mental status tests were administered four times: times 1 and 2 at CSHA-1 and times 3 and 4 at CSHA-2. Mean difference scores and percent of baseline scores showed relatively small group changes over both short and long test-retest intervals for the MMSE and the 3MS. A reliable change index based on a linear regression model controlled for practice effects, psychometric errors due to low reliability, regression to the mean, and accounted for the effects of various demographic variables. Consequently, this reliable change index provided a better estimate of the amount of change that occurred for individual participants than did the mean Retest-Test 1 difference, percent of baseline change, or a reliable change index based on a Retest-Test 1 difference score. Normative data for the change scores are provided.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".