Vascular Parkinsonism in a Tertiary Care Stroke Prevention Clinic: Prevalence and Development of a New Screening Strategy (P3.087)
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of Vascular parkinsonism (VasP) in a Stroke Prevention Clinic (SPC), and the usefulness of a two step process involving the Tanner Questionnaire (TQ) and a Five Minute Assessment Score (FMAS) for the diagnosis of VasP. BACKGROUND: VasP can be defined by parkinsonism with prominent gait problems occurring within one year of stroke. Few prevalence studies have been completed in stroke prevention clinics. DESIGN/METHODS: Consecutive stroke patients were screened in the SPC, during a five month period with a validated questionnaire (TQ) for detection of parkinsonism using a cut off of 蠅4. We applied Zijlman’s criteria for the diagnosis of VasP (2004), which were operationalized using a newly developed 4 point, Five Minute Assessment Score (FMAS). All patients were clinically interviewed and examined to confirm parkinsonism. RESULTS: One hundred and five patients (48% males and 52% females) were screened and examined, with a mean age of 66 (range 25 - 95; SD 14.5). Nineteen patients (18%) scored 蠅 4 on the TQ (p<0.0001). Of the 19 patients, 9 (47%) had parkinsonism. Only 5 (26%) fulfilled the clinical criteria for VasP by attaining a score of 4 in the FMAS. Prevalence of parkinsonism in the SPC was 9/105 (8.5%), with prevalence of VasP being 5/105 (4.7%). A cut off score of 蠅4 in the TQ gave a sensitivity of 100%, and specificity of 92%. The positive predictive value was 54%, with a likelihood ratio for a positive result of 10.6. CONCLUSIONS: We documented a prevalence of 4.7% for VasP in a tertiary care SPC. The TQ had a high sensitivity and specificity to screen for Parkinsonism when the cut off 蠅4 was used. The FMAS was helpful to confirmVasP and distinguish it from the other forms of parkinsonism, including IPD. The two-step TQ-FMAS screening might be a useful strategy to identify promptly patients with VasP. Study Supported by: the Toupin Foundation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".