C - 51Specificity of Reliable Digit Span in Dementia Evaluations
Bibliographic record
Abstract
Objective: Reliable Digit Span (RDS) is the most commonly used embedded performance validity test. The present study investigated the specificity of RDS in an older adult outpatient sample referred for dementia evaluation across three levels of cognitive impairment severity. Method: Archival data from 117 patients was utilized (mean age = 73.52; mean education = 13.45 years). Patients were clinically diagnosed with either mild cognitive impairment (MCI; n = 42) or dementia (n = 75). Montreal Cognitive Assessment performance was used to classify participants with dementia as either mild (n = 55) or moderate (n = 20) severity via a cutoff of 14. Mean Repeatable Battery for the Assessment of Neuropsychological Status Total Scale scores for MCI, mild dementia, and moderate dementia groups were 86.24, 71.65, and 57.75, respectively. Results: Using a cutoff of <6, specificity rates were 90%, 83.6%, and 60% for the MCI, mild dementia, and moderate dementia groups, respectively. To maintain specificity of at least 90% in the mild dementia group, a cutoff of <5 was necessary. To maintain 90% specificity in the moderate dementia group, a cutoff of <3 was required. Conclusions: Specificity findings within the mild dementia sample of heterogeneous etiology and MCI correspond with those published by Loring et al. (2016) within early AD and MCI samples, respectively. The current findings provide further evidence that a cutoff of <6 is appropriate in patients with MCI, and indicate that a more conservative cutoff is required in patients with mild dementia regardless of etiology. RDS is not useful in patients suspected of moderate dementia given the need for a substantially lowered cutoff score.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".