How impaired is too impaired? Exploring futile neuropsychological test patterns as a function of dementia severity and cognitive screening scores
Bibliographic record
Abstract
Some older adults cannot meaningfully participate in the testing portion of a neuropsychological evaluation due to significant cognitive impairments. There are limited empirical data on this topic. Thus, the current study sought to provide an operational definition for a futile testing profile and examine cognitive severity status and cognitive screening scores as predictors of testing futility at both baseline and first follow‐up evaluations. We analysed data from 9,263 older adults from the National Alzheimer’s Coordinating Center Uniform Data Set. Futile testing profiles occurred rarely at baseline (7.40%). There was a strong relationship between cognitive severity status and the prevalence of futile testing profiles, χ 2 (4) = 3559.77, p < .001. Over 90% of individuals with severe dementia were unable to participate meaningfully in testing. Severity range on the Montreal Cognitive Assessment (MoCA) also demonstrated a strong relationship with testing futility, χ 2 (3) = 3962.35, p < .001. The rate of futile testing profiles was similar at follow‐up (7.90%). There was a strong association between baseline dementia severity and likelihood of demonstrating a futile testing profile at follow‐up, χ 2 (4) = 1513.40, p < .001. Over 90% of individuals with severe dementia, who were initially able to participate meaningfully testing, no longer could at follow‐up. Similarly, there was a strong relationship between baseline MoCA score band and likelihood of demonstrating a futile testing profile at follow‐up, χ 2 (3) = 1627.37, p < .001. Results can help to guide decisions about optimizing use of limited neuropsychological assessment resources.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".