Cognition and olfaction in Parkinson’s disease
Bibliographic record
Abstract
Sir, We read with interest the recent paper in Brain by Bohnen et al. (2010), which suggested correlation between cognitive function and olfactory loss in Parkinson’s disease (Bohnen et al., 2010). They found that among 58 patients with Parkinson’s disease, those with poor episodic verbal memory had more impaired olfaction, as tested by the University of Pennsylvania Smell Identification Test (UPSIT). Additionally, there was borderline correlation between olfaction and non-verbal memory and Mini-Mental State Examination. We are conducting an ongoing study of non-motor manifestations of Parkinson’s disease, which includes a full neuropsychological examination and olfactory testing (Gagnon et al., 2009; Postuma et al., 2009). This allows us to quickly assess if these findings can be replicated. We have examined a group of 71 patients with idiopathic Parkinson’s disease. Mean age was 66.3 years and 50 (71%) were males. Disease duration was 5.0 ± 3.4 years, Unified Parkinson Disease Rating Scale Part 3 was 22.6 ± 10.9, Mini-Mental State Examination was 28.3 ± 1.8 and mean UPSIT was 17.9 ± 6.2. Neuropsychological examination was divided into four summary composite components: (i) attention/executive functions (Trail Making Test part B, Stroop 3 minus Stroop 1 time, semantic verbal fluency, phonetic verbal fluency); (ii) episodic verbal memory (Rey Auditory Verbal Learning test—sum of trials 1–5, List B, immediate recall and delayed recall); (iii) non-verbal memory (Rey-O figure immediate and delayed recall); and (iv) visuoconstructional abilities (Copy of the Rey-O figure). The 40-item UPSIT was performed according to standard criteria. Regression analysis was used to adjust for age and disease duration (sex P = 0.19 and education P = 0.61) were not associated with UPSIT in the regression model (R with all four variables = 0.515, with age and duration only = 0.495), and so were not included in the model). Acetylcholinesterase PET scanning was not performed in this cohort.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".