Operationalizing Impaired Performance in Neuropsychological Assessment: A Comparison of the Use of Published <i>Versus</i> Sample-Based Normative Data for the Prediction of Dementia
Bibliographic record
Abstract
OBJECTIVES: To compare the sensitivity, specificity, and predictive value of published versus sample-based norms to detect early dementia in the Uniform Data Set (UDS). METHODS: The UDS was administered to 526 nondemented participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Baseline scores were standardized using published norms and healthy control data from ADNI corrected for age, education, and sex. Subjects obtaining two scores < -1 SD (determined separately using published and sample norms) were labeled "at risk for dementia." Both methods were compared on sensitivity, specificity, and positive/negative predictive value (PPV/NPV) for dementia at follow-up. RESULTS: Risk scores derived from published data had 86.1% sensitivity, 62.0% specificity, 68.6% accuracy, 46.1% PPV, and 92.2% NPV. Those from sample norms were more sensitive (91.0%), less specific (52.9%), and less accurate (63.3%), with worse PPV (42.1%) and similar NPV (94.0%). Sample norms were better at identifying incident dementia cases with relatively lower education than those with higher education. Discrepancies between both methods were more common in women. CONCLUSIONS: Sample norms are marginally more sensitive than published norms for predicting dementia, while published norms are slightly more accurate. Accuracy of risk estimates for women and those with lower education may be increased using locally generated norms.
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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 | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".