Association of OFF period characteristics with patient communication behaviors in Parkinson’s disease
Bibliographic record
Abstract
Objective Underreporting of OFF symptoms and poor patient-provider communication are known challenges in Parkinson’s disease (PD). We aimed to determine: (a) OFF period patient communication behavior components and (b) whether OFF period characteristics (frequency, duration, severity) were associated with communication behaviors.Methods A retrospective cohort study using the Fox Insight database was conducted. OFF periods were characterized by frequency (number of episodes/day), duration (duration/episode), and severity (impact on activities). The “Impact and Communication about OFF periods” questionnaire data were subjected to principal components analysis. Generalized linear model regressions with log link function were used to identify associations with OFF period characteristics.Results Data from 526 respondents showed that 89% were non-Hispanic whites, mean age = 65 ± 8.9 years, with PD duration = 6 ± 4.8 years. The majority (67%) had a frequency of 1–2 OFF periods/day, with the highest percent (31.4%) having 15–30 min OFF periods duration, and majority (63%) having none-to-some OFF periods severity. Two component communication behaviors were identified: (1) reluctance communicating (7 items, Cronbach’s alpha (CA) = 0.851) and difficulty communicating (4 items, CA = 0.857). Above-average OFF periods severity was associated with a 12% higher reluctance score (beta = 0.117, 95% confidence interval (CI) = 0.03–0.21, p = 0.024). Higher OFF periods frequency and above-average severity were associated with greater difficulty communicating (Frequency: beta = 0.125, 95% CI = 0.03–0.22, p = 0.024; Severity: (beta = 0.186, 95% CI = 0.08–0.29, p = 0.003)) about OFF periods.Conclusion Patient communication about OFF periods involves two main factors: their difficulty and reluctance to communicate. Greater frequency and severity of OFF periods were associated with greater difficulty and reluctance to communicate. Understanding these relationships can guide providers to take preemptive efforts to promote OFF periods communication, enhancing care quality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".