Missing Dosages and Neuroleptic Usage May Prolong Length of Stay in Hospitalized Parkinson's Disease Patients
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease patients are more likely to be hospitalized, have higher rates of hospital complications, and have an increased risk of deterioration during hospitalization. Length of stay is an important underlying factor for these increased risks. We aimed to investigate potential medication errors that may occur during hospitalization and its impact on length of hospital stay. METHODS: A cross-sectional chart review of 339 consecutive hospital encounters from 212 PD subjects was performed. Medication errors were defined as wrong timing or omission of administration for dopaminergic drugs and administration of contraindicated dopamine blockers. An analysis of covariance was applied to examine whether these medication errors were related to increased length of hospital stays. RESULTS: A significant effect for dopaminergic administration (p<0.01) on length of hospital stay was observed. Subjects who had delayed administration or missed at least one dose stayed longer (M=8.2 days, SD=8.9 vs. M=3.6 days SD=3.4). Contraindicated dopamine blocking agents were administered in 23% (71/339) of cases, and this was also significantly related to an increased length of stay (M=8.2 days, SD=8.9 vs. M=3.6 days SD=3.4), p<0.05. Participants who received a contraindicated dopamine blocker stayed in the hospital longer (M=7.5 days, SD=9.1) compared to those who did not (M=5.9 days, SD=6.8). Neurologists were consulted in 24.5% of encounters. Specialty consultation had no effect on the medication related errors. CONCLUSIONS: Missing dopaminergic dosages and administration of dopamine blockers occur frequently in hospitalized Parkinson's disease patients and this may impact length of stay. These potentially modifiable factors may reduce the risk of a longer stay related to hospitalization.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".