Estimation of Cognitive Performance Based on Premorbid Intelligence in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: The estimation of premorbid intelligence (PI) is needed for an accurate diagnosis. OBJECTIVE: This study aimed to estimate the cognitive performance taking into account the PI in Parkinson's disease (PD) compared to healthy controls (HC); and to analyze the discrepancies between the current and the predicted cognitive performance based on the PI. METHOD: Semantic fluency, verbal and visual memory, and executive functions were assessed in 39 PD and 162 HC. A linear regression model was used to analyze the discrepancies between the predicted cognitive performance and the current raw scores through PI variables (Word Accentuation Test (WAT), Pseudo-Words (PW) Reading subtest from PROLEC-R, age, and years of education). ROC analyses were performed to assess their diagnostic properties. RESULTS: Significant differences were found in the raw cognitive scores between patients and HC [semantic fluency (t = 6.07; p < 0.001), verbal memory (t = 6.63; p < 0.001), and executive functions (t = 2.57; p = 0.013), and in visual memory (t = 1.97; p = 0.055 marginally significant)]. Compared to HC, PD patients presented higher discrepancies between the predicted cognitive performance and the raw scores in semantic fluency, verbal memory, visual memory, executive functions (AUC = 0.78, 0.78; 0.64, 0.61, respectively). CONCLUSION: The magnitude of the discrepancies scores between the current and the predicted cognitive performance based on PI indicates the presence of cognitive decline in the specific cognitive domain in PD patients. This study underlines the usefulness of premorbid measures and variables, such as WAT, PW, age, and years of education, to more accurately estimate the cognitive performance in PD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".