Increasing Sensitivity in Patient‐Reported <scp>MDS</scp>‐<scp>UPDRS</scp> Items for Predicting Medication Initiation in Early <scp>PD</scp>
Bibliographic record
Abstract
BACKGROUND: The MDS-UPDRS Parts IB and II are self-reported items providing a direct patient voice to the experiences of PD. OBJECTIVE: To determine the most sensitive combination of MDS-UPDRS Parts IB and II items that accurately predicted the clinically relevant target of dopaminergic therapy initiation. METHODS: Utilizing a longitudinal cohort of de novo non-treated PD patients, we applied item response theory (IRT) and survival analysis to assess the relationship between baseline patient-reported symptoms and the later initiation of dopaminergic therapy. The 20 MDS-UPDRS Parts IB and II items were analyzed for their relationship to PD severity (discrimination) and the amount of information they provided in this determination (information). These parameters were used to develop models of predictive accuracy for initiation of dopaminergic therapy. RESULTS: A six-item version showed a significantly higher C-index as compared to the full 20 item model (P = 0.001). This shortened version of the MDS-UPDRS contained only Part II items and provided a predictive accuracy for initiation of dopaminergic therapy better than the total combined scale score or any other combination. CONCLUSIONS: A six-item "Baseline Outcome Voice" version of patient-reported MDS-UPDRS items significantly increases the sensitivity of predicting the key future clinical outcome of starting dopaminergic treatment in early PD. This study also demonstrates how IRT modeling can provide information useful to refining existing measures to identify the most sensitive combination of items honoring the voice of the patient in determining key clinically pertinent decisions. Further research is needed to validate these findings in underrepresented populations.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".