Premorbid IQ Influence on Screening Tests’ Scores in Healthy Patients and Patients With Cognitive Impairment
Bibliographic record
Abstract
Cognitive screening tests are well-established tools for detecting cognitive impairment, but concerns regarding the influence of premorbid intelligence on patient's performance and cognitive status classification remain. Risk of inaccurate assessment especially affects the elders with high or low premorbid intelligence (who are more likely to be misclassified). The present study examines the influence of premorbid intelligence assessed by the TeLPI (an irregular words reading test) on 2 cognitive screening tests, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), in healthy participants and patients with cognitive impairments (mild cognitive impairment and Alzheimer disease). Results show that premorbid IQ influences the MMSE and the MoCA scores in both the groups, predicting variance from 8.4% to 33.2%, according to test and group analyzed. Hence, we propose that whenever the MMSE or the MoCA is used, premorbid IQ evaluation should also be considered to ensure correct interpretation and classification.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".