Referring Parkinson’s disease patients for deep brain stimulation: a RAND/UCLA appropriateness study
Bibliographic record
Abstract
In 2005, a European expert panel developed and validated an electronic tool to support the appropriate referral of patients with Parkinson's disease (PD) for the consideration of deep brain stimulation (DBS). Since new evidence has become available over the last decade an update of the tool is necessary. A world-wide expert panel (71 neurologists and 11 neurosurgeons) used the RAND/UCLA Appropriateness Method to assess the appropriateness of referral for 1296 scenarios (9-point scale). Scenarios were permutations of 8 clinical variables relevant to the decision of referral. Appropriateness of referral was calculated on the basis of the median score and the extent of agreement. Compared to 2005, the impact of clinical variables on the appropriateness of referral was similar for severity of on-off fluctuations, dyskinesias and refractory tremor (positive association, p < 0.001), and cognitive impairment (negative association, p < 0.001). A relatively stronger negative impact was seen for levodopa-unresponsive gait and balance disturbances as well as older age, the latter most likely due to a higher cut-off value (75 versus 70 years in the previous study). The impact of PD duration on the appropriateness of referral was less pronounced than in 2005. The contribution of the newly included variable 'non-motor side effects of anti-PD medication' was very modest. Based on these results the panel produced new recommendations on the appropriateness of referral for the evaluation of DBS in PD patients. Differences from the previous study reflect the new clinical evidence, particularly related to the use of DBS in an earlier stage of PD. The validation of the updated recommendations is in progress.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".