WED 130 Designing a care pathway for high risk parkinson’s patients
Bibliographic record
Abstract
Objective To inform pathway design for patients at high risk of an adverse outcome at 5 years post-Parkinson’s disease (PD) diagnosis. Background In our service, all newly diagnosed patients follow a 12 month new patient pathway, which includes assessment with PDQ-39, NMS Quest and MOCA. Use of a prognostic calculator1 allows for refinement of the pathway according to patient need. Patients are classified as low (0–0.3), medium (0.31–0.79) or high (0.8–1) risk of an adverse outcome at 5 years (postural instability, dementia, death). Aim We aim to evaluate whether assessments in the newly diagnosed pathway identify additional care needs in higher risk patients. Methods An evaluation was carried out of our new patient database. Individuals who had the prognostic indicator performed (n=52) were included in the evaluation. Results Patients in the high risk groups were found to be older and more cognitively impaired than lower risk patients (p<0.05). MOCA scores negatively correlated with NMS Quest (r=−0.44, p<0.001) and PDQ-39 (r=−0.51, p<0.001) suggesting as cognition becomes impaired, NMS burden increases, and quality of life is reduced. Conclusions Additional care needs were identified in high-risk patients. These findings highlight additional resources are required in this patient cohort to ensure needs are met. Reference . Velseboer DC, et al. Development and external validation of a prognostic model in newly diagnosed Parkinson disease.
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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 | 0.003 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".