The added value of anosmic subtype on motor subtype in Parkinson’s disease: a pilot study
Bibliographic record
Abstract
This study investigates whether incorporating olfactory dysfunction into motor subtypes of Parkinson's disease (PD) improves associations with clinical outcomes. PD is commonly divided into motor subtypes, such as postural instability and gait disturbance (PIGD) and tremor-dominant PD (TDPD), but non-motor symptoms like olfactory dysfunction remain underexplored. We assessed 157 participants with PD using the University of Pennsylvania Smell Identification Test (UPSIT), Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (M-UPDRS), Montreal Cognitive Assessment (MoCA), 39-item Parkinson's Disease Questionnaire Summary Index (PDQ-39 SI), and 99mTc-TRODAT-1 imaging. Motor subtypes were categorized as PIGD and TDPD, and olfactory subtypes were categorized as total anosmia (TA) and non-anosmia (NA). Significant differences were observed, with the highest disease burden occurring in PIGD TA, while the lowest occurred in TDPD NA. The total M-UPDRS scores (59.0, 47.5, 43.0, 36.0; p < 0.001) and PDQ-39 SI scores (22.4, 22.8, 9.6, and 9.0; p < 0.001) varied significantly across groups, and the highest occurred for PIGD TA, followed by PIGD NA, TDPD TA, and TDPD NA. MoCA scores indicated the best cognitive performance in TDPD NA (p = 0.002). Thus, the results show that integrating olfactory dysfunction with motor subtypes may enhance PD classification, particularly in cognitive assessment in cases of TDPD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".