Sniffing out cognitive decline in patients with and without evidence of dopaminergic deficit
Bibliographic record
Abstract
BACKGROUND: Depletion of dopamine is a major neuropathological feature of Parkinson's disease; however, 15% of patients with parkinsonian motor symptoms have neuroimaging evidence of intact dopaminergic function. Recent work has demonstrated that such patients without dopaminergic deficit are at a greater risk of cognitive impairment yet have intact olfaction relative to parkinsonian patients with dopaminergic deficit. OBJECTIVES: Given the high discriminatory power of olfaction assessments in movement disorders, the current study sought to determine whether olfaction dysfunction differentially predicted cognitive decline in patients with or without dopaminergic deficit. METHODS: Data were obtained from the Parkinson's Progression Marker Initiative. The total sample included 401 patients with and 51 patients without dopaminergic deficit, based on neuroimaging scans, and 175 healthy controls. Participants were categorized into non-impaired or impaired olfaction groups based on performance on the University of Pennsylvania Smell Identification Test. Participants were administered the Montreal Cognitive Assessment twice (baseline and two-year follow-up), and change scores were calculated to examine changes in cognition over time. RESULTS: Within the impaired olfaction groups, participants without dopaminergic deficit had lower cognitive scores than participants with dopaminergic deficit and healthy controls at baseline. Group differences were not significant at follow-up; rather, impaired baseline olfaction predicted cognitive decline across all study participants. CONCLUSIONS: Future studies are needed to assess whether the profile of motor and non-motor symptoms in patients without dopaminergic deficit, including olfaction, are deserving of their own syndrome, or whether individual patients may fit better under alternative, existing diagnoses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".