[P4–299]: BASE RATES OF IMPAIRED SCORES ARGUE AGAINST CORRECTION FOR MEDICAL CONDITIONS WITHIN A BRIEF NEUROPSYCHOLOGICAL BATTERY
Bibliographic record
Abstract
Normative data are usually created by making demographic adjustments based on correlations between single neuropsychological tests with demographic variables such as age and education. However, this traditional approach has some limitations. For example, it does not detail the implications for clinical practice such as likelihood of misclassification of cognitive impairment. It also does not elucidate the impact on decision making when using a neuropsychological battery. We hypothesize that baserates analyses, particularly, differential baserates of impaired scores between theoretical and actual baserates, would be better suited to create demographic adjustments within normative data. Differential baserates empirically detail the potential clinical implications of failing to create an appropriate normative group. Thus the aim of the present study is to explore whether adjustments for age and medical conditions are warranted based on differential baserates of spuriously impaired scores. The present study uses telephone-administered data collected from over 20,000 participants of the Canadian Longitudinal Study on Aging (CLSA). The CLSA has recruited a large national sample of approximately 50,000 French- and English-speaking men and women between the ages of 45 and 85 across 10 Canadian provinces; over 20,000 responded through telephone interview and 30,000 to an in-home interview and physical assessments at a data collection site. Differential baserates are calculated using the results of a short neuropsychological battery administered by telephone to a neurologically healthy sample (but only the English-speaking sub-sample is used in the present research). Theoretical baserates underestimate the frequency of impaired scores in older adults, providing an evidence base for the creation of age corrected normative data. Interestingly, several medical conditions such as cancer, chronic obstructive pulmonary disease, cardiovascular (e.g., high blood pressure, angina, acute myocardial infarction), hormonal (e.g., hypo- and hyperthyroidism) and metabolic (e.g., diabetes) conditions are not associated to differential baserates of impaired scores. Despite a small magnitude correlation between number of medical conditions and each neuropsychological variable, normative adjustments for number of medical conditions does not appear warranted.
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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 | 0.044 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".