Verbal memory in drug-naive, newly diagnosed Parkinson's disease. The retrieval deficit hypothesis revisited.
Bibliographic record
Abstract
OBJECTIVE: The retrieval deficit hypothesis on memory impairment in patients with Parkinson's disease (PD) implies a selective impairment in recall of learned material with normal encoding, retention, and recognition. This hypothesis has been challenged by new data. We have therefore investigated verbal memory and learning in a large sample of newly diagnosed, drug naïve, non-demented patients with PD. METHOD: From a sample of patients with PD from the Norwegian ParkWest study, 133 PD patients and 133 controls matched on sex, age, and education were included. The California Verbal Learning Test-2 (CVLT-2) was used to assess verbal memory. RESULTS: Patients performed significantly worse than controls on free and cued recall as well as on recognition memory. Patients used the semantic clustering learning strategy significantly less extensively than the controls and the learning slope of the PD patients was significantly less steep. There was no difference in retention when controlling for encoding. Patients did not perform better on the recognition measure or on cued recall (d-prime), as compared to free recall. Executive functions explained a substantial part of the memory deficits. CONCLUSIONS: This study suggests that memory impairment in drug naïve early PD to a large degree is a deficit of learning/ encoding and not of retention or retrieval. An implication is that the retrieval deficit hypothesis should be moderated in its general form. Executive deficits and less extensive use of the efficient semantic clustering learning strategy had a strong impact on learning and memory.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".