Bibliographic record
Abstract
Introduction: Identify the non-decoded network in Vascular Parkinsonism (VasP). Objective: To determine what pattern of stroke lesions is responsible for VasP, as compared to those patients who had stroke, gait and balance problems, but absence of a hypokinetic rigid syndrome also called Vascular Pseudo Parkinsonism-V PSeuP. Materials and Methods: Design: prospective cohort study. Participants were consecutively screened for parkinsonian symptoms during a year as according to our previous study. Validated questionnaire (Tanner Questionnaire-TQ) was used, and a new scale operationalizing the criteria for VasP (FMAS score). All participants in the original study had a clinical exam to identify if presence of a hypokinetic rigid syndrome. Lesion patterns were analyzed. Setting: tertiary care stroke prevention clinic at the University of Alberta Hospital. Participants: Eligible participants attained a score of ≥ 4 on the TQ, high risk for parkinsonism. Four groups were considered: V PseuP, VasP (onset of parkinsonism within a year of the stroke -FMAS score of 4), Pseudo Vascular Parkinsonism-PseuVP (hypokinetic rigid syndrome not related to stroke), and Pseudo Vascular Pseudo Parkinsonism-PseuV PseuP (no stroke and no extrapyramidal syndrome), but with gait and balance problems. Baseline demographic information and clinical characteristics were recorded including vascular risk factors, and stroke subtype. All participants had a Holter, CT head and/or brain MRI, and CTA. Medications that produce drug-induced-parkinsonism were recorded for every participant. The primary outcome was to find the pattern of anatomical lesions particularly involved in the VasP subgroup considering the Basal Ganglia Motor Output Circuit-BGMO, the Thalamo Cortical Drive Loop-TCD and connections to frontal cortex. Results: 240 consecutive participants were screened during 12 months. We found 46 patients with potential Parkinsonism (TQ>4). VPseuP was found in 25/46 (54%), VasP in 8/46 (17%), PseuVP in 7/46 (15%), and PseuV PseuP in 6/46 (14%). VasP were older (p<0.0007) and had a higher risk for cardio embolism due to atrial fibrillation (p=0.02, odd ratio 6.6 CI 95% (1.2 – 35.2)). Neuro images showed that only the pattern involving the BGMO and frontal cortex was significantly associated to the group of VasP (X2 Fisher exact test p<0.0005 Odds ratio 32 CI 95% (9.6-108)); whereas the pattern TCD was not significantly different between the groups (X2 Fisher exact test p=0.828 Odds ratio 1.2 CI 95% (0.5-2.8)). Discussion and Conclusion: A two strategic location hit within the BGMO circuit and frontal cortex is required, so a phenotype of VasP may occur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".