Medication Ordering Practices for Parkinson's Disease Patients Admitted to Hospital (S02.007)
Bibliographic record
Abstract
Objective: In this study we evaluated medication reconciliation and ordering of dopamine blocking medications in Parkinson9s disease (PD) patients admitted to hospital. Background Patients with PD are often admitted to acute care hospitals with a variety of medical and surgical issues. Their care providers in such situations are often not the physicians in charge of their PD care, and as a result, there is a potential for lack of familiarity with PD treatment regimens. Design/Methods: Patients of the Movement Disorders Clinic residing in Calgary with PD were cross-referenced with the Calgary hospital admissions database for 2010. Medication reconciliation of PD medications at admission was examined for medication total dosage, scheduling, omission, and formulation (either long acting(CR), or immediate release(IR)). Ordering of dopamine blocking medications, either metoclopramide or neuroleptics was also assessed. Results: 403 patients of the MDC were screened. 55 admissions for 44 PD patients were analyzed. In 44/55(80%), medication reconciliation discrepancies or prescription of dopamine blocking medications were noted. 53/55(96.4%) of admissions were for patients taking levodopa. Of these 53, 26/53(49.0%) had differences between pre-admission and admission levodopa orders: in 3/53(5.7%) no levodopa was ordered; 15/53(28.3%) had dosage differences; 8/53(15.1%) had scheduling differences; and 12/53(22.6%) were ordered the incorrect formulation. 19/55(34.5%) of admissions were for patients who were on PD medications in addition to levodopa:7/19(36.8%) had differences in the ordering of these medications at the time of admission. Dopamine blocking agents were ordered during 24/55(43.6%) admissions. Conclusions: Our study identifies deficiencies in the in-hospital prescribing of medications for patients with PD. This includes a failure to seamlessly continue usual PD medications when patients are admitted, and perhaps of even greater concern, the inappropriate addition of dopamine blocking agents during their hospital stay. Widespread education of providers and safe-prescribing protocols are urgently needed to address these unsafe care issues. Disclosure: Dr. Wiltshire has nothing to disclose. Dr. Furtado has nothing to disclose. Dr. Ghali has nothing to disclose. Dr. Kraft has nothing to disclose.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".