Improving Documentation of Impulse Control Disorders at a Movement Disorder Program During the COVID-19 Pandemic
Bibliographic record
Abstract
Background and Objectives: Impulse control disorders (ICD) are a group of behaviors in Parkinson disease (PD), (compulsive buying, gambling, binge eating, craving sweets, and hypersexuality) that occur in up to 20% of individuals with PD, sometimes with devastating results. We sought to determine the rate of ICD screening based on 2020 quality measures for PD care by the American Academy of Neurology. Methods: We conducted a quality improvement project to document and improve physician ICD screening in a tertiary movement disorder program. Serial medical records were reviewed for 5 weeks before and 13 weeks after an educational session and documentation tool deployments in 2020. Inclusion criteria included the following: idiopathic PD, PD dementia (PDD), or dementia with Lewy bodies (DLB). Individual encounters for 109 patients preintervention and 276 patients postintervention were reviewed. Results: = 0.444). Discussion: ICD queries immediately after ICD education and dissemination of documentation tools increased. Both preintervention and postintervention groups were similar in demographic and clinical characteristics. This program was instituted at the height of wave 2 of the COVID-19 pandemic in Alberta during staff redeployment and 100% shift to telemedicine ambulatory care. Our results demonstrate that amid a crisis, quality improvement can still be effective with education and provision of tools for clinicians.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".