30 Examining the Base Rates of Low Scores in Older Adults with Subjective Cognitive Impairment from a Specialist Memory Clinic
Bibliographic record
Abstract
Objective: Cognitively healthy individuals who complete a neuropsychological test battery can obtain very low scores. These very low scores are not likely indicative of cognitive impairment but are rather considered spuriously low scores. The expected number of low scores varies based on number and type of neuropsychological tests. Typically, base rates have been determined from normative samples, which could differ from samples seen in clinical settings. The current study reports on base rates of spuriously low cognitive scores in older adults presenting to a memory clinic who were diagnosed with subjective cognitive impairment after interprofessional assessment and information from collateral informants ruled out objective cognitive impairment. Participants and Methods: Base rates of spuriously low scores for a neuropsychological battery of 12 scores were based on 92 cognitively healthy older adults presenting to a specialist memory clinic (M(age) = 61.00, SD = 12.00; M(edu) = 12.00, SD = 2.74). Crawford’s Monte Carlo simulation algorithm was used to estimate multivariate base rates by calculating the percentage of cognitively healthy memory clinic patients who produced age and education normed scores at or below the 5th percentile. The following tests were used to produce the 12 scores: block design, digit span backwards, and coding from the WAIS-IV; logical memory I and II from the WMS-IV; immediate and delayed memory scores from the California Verbal Learning Test Second Edition short form; immediate and delayed memory scores from the Brief Visuospatial Memory Test Revised; category switching, letter number sequencing, and inhibition switching from the Delis Kaplin Executive Functioning System. Results: An estimated 33.58% of the cognitively healthy memory clinic population would have one or more low scores (5th percentile cutoff),14.7% would have two or more low scores, 6.55% would have three or more, 2.94% would have four or more, and 1.31% percent would have 5 or more very low scores due to chance. Conclusions: Determining base rates of spuriously low scores on a neuropsychological battery in a clinical sample of referred older adults with subjective memory complaints could assist in the diagnostic process. By understanding base rates of clinical samples, clinicians can use empirical data to adjust for expected low scores rather than using conventional corrections (such as 1/20 test scores expected to be low). In a memory clinic sample, three or more low test scores out of 12 is expected to be relatively rare in those who were later determined to have no objective evidence of cognitive impairment based on interprofessional assessment. Understanding normal frequency of low scores will prevent undue conclusions of cognitive impairment which will minimize false positives in diagnosis.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".