The Coin-in-the-Hand Test and Dementia
Bibliographic record
Abstract
BACKGROUND: The Coin-in-the-Hand Test was developed to help clinicians distinguish patients who are neurocognitively impaired from patients who are exaggerating or feigning memory complaints. Previous findings have shown that participants asked to feign memory problems and patients suspected of malingering performed worse on the test than patients with genuine neurocognitive dysfunction. OBJECTIVE: We reviewed the literature on the Coin-in-the-Hand Test and evaluated test performance by 45 hospitalized patients who had dementia with moderately to severely impaired cognition. METHODS: We analyzed Coin-in-the-Hand Test scores, neuropsychological findings, and other data to determine whether demographic or neurocognitive variables affected Coin-in-the-Hand Test scores. We also calculated base rates of these scores and provided cutoff ranges for clinical use. RESULTS: Coin-in-the-Hand Test scores were independent of neurocognitive functioning, age, education level, and type of dementia. Base rates of scores suggest that a low cutoff can help differentiate between patients with true neurocognitive impairments and those exaggerating or feigning memory complaints. CONCLUSIONS: Both the literature and our findings show the Coin-in-the-Hand Test to have potential as a quick and easy screening tool to detect neurocognitive symptom exaggeration. This test could effectively supplement commonly used neurocognitive screens such as the Mini-Mental State Examination, the Saint Louis University Mental Status Examination, and the Montreal Cognitive Assessment.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".