An evidence-based approach to the creation of normative data: base rates of impaired scores within a brief neuropsychological battery argue for age corrections, but against corrections for medical conditions
Bibliographic record
Abstract
OBJECTIVE: We detail a new approach to the creation of normative data for neuropsychological tests. The traditional approach to normative data creation is to make demographic adjustments based on observations of correlations between single neuropsychological tests and selected demographic variables. We argue, however, that this does not describe the implications for clinical practice, such as increased likelihood of misclassification of cognitive impairment, nor does it elucidate the impact on decision-making with a neuropsychological battery. METHOD: We propose base rate analyses; specifically, differential base rates of impaired scores between theoretical and actual base rates as the basis for decisions to create demographic adjustments within normative data. Differential base rates empirically describe the potential clinical implications of failing to create an appropriate normative group. We demonstrate this approach with data from a short telephone-administered neuropsychological battery given to a large, neurologically healthy sample aged 45-85 years old. We explored whether adjustments for age and medical conditions were warranted based on differential base rates of spuriously impaired scores. CONCLUSIONS: Theoretical base rates underestimated the frequency of impaired scores in older adults and overestimated the frequency of impaired scores in younger adults, providing an evidence base for the creation of age-corrected normative data. In contrast, the number of medical conditions (numerous cardiovascular, hormonal, and metabolic conditions) was not related to differential base rates of impaired scores. Despite a small correlation between number of medical conditions and each neuropsychological variable, normative adjustments for number of medical conditions does not appear warranted. Implications for creation of normative data are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".