Minimizing Misdiagnosis: Psychometric Criteria for Possible or Probable Memory Impairment
Bibliographic record
Abstract
BACKGROUND/AIMS: Memory impairment can be easily misdiagnosed in older adults because obtaining some low scores is common. The objective of the present study is to present new psychometric criteria for determining 'possible' and 'probable' memory impairment. METHODS: We propose criteria based on an analysis of performance from 450 healthy older adults (55-87 years old) on 3 measures from the WMS-III: Logical Memory, Word List, and Visual Reproduction. These measures yield 8 age-adjusted scores for learning, recall, and recognition. The proposed criteria for memory impairment are based on the prevalence of low scores when simultaneously examining all 8 scores and are stratified by current intelligence, estimated premorbid intelligence, and education. The criteria are subsequently validated on 100 healthy older adults and 34 patients with 'possible' or 'probable' Alzheimer's Disease (AD). RESULTS: Tables with cutoffs and false-positive rates are presented for clinical use. In the validation cohort there were no misclassifications in AD patients. CONCLUSION: This study presents steps in the development of proposed psychometric criteria that, in conjunction with clinical judgment, could minimize the misdiagnosis of memory impairment. It is important to reduce misdiagnosis in order to (a) optimize patient care, (b) provide an accurate foundation for identifying biological and neurological markers, and (c) successfully develop disease-modifying treatments. Further validation in a sample of older adults with lesser degrees of cognitive impairment is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".