To Change is Human: “Abnormal” Reliable Change Memory Scores are Common in Healthy Adults and Older Adults
Bibliographic record
Abstract
OBJECTIVE: The rate at which people obtain reliably improved or declined cognitive test scores when retested, in the absence of a change in clinical condition, is largely unknown. The purpose of this study was to illustrate the prevalence of statistically reliable change scores on memory test batteries in healthy adults and older adults. METHOD: Participants included three adult and older adult test-retest samples from memory test batteries. Reliable change scores (reliable change index with 90% confidence interval and practice effects) were calculated for the indexes and subtests of each battery. Multivariate analyses involved calculating the frequencies of healthy people obtaining one or more reliably declined or one or more reliably improved scores when considering all change scores simultaneously within each battery. RESULTS: Across all batteries, having one or more reliably changed index or subtest score on retest was common. With most batteries, having two or more reliably changed scores was uncommon. Those with higher intellectual abilities were more likely to have a change on retest; however, no significant differences in base rates were found based on education level, sex, or ethnic minority status. Those older adults who did not have any low memory scores were more likely to improve than decline on retest. CONCLUSIONS: Having a single reliably changed score on retest is common when interpreting a battery of memory measures. This has implications for determining cognitive decline and cognitive recovery, suggesting that multivariate interpretation is necessary.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".