Telehealth Management in Movement Disorder: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Management of chronic diseases such as movement disorders can be challenging. Nurse-administered telephone follow-up programs have demonstrated clinical and cost efficacy in a variety of health care models. However, their efficacy in movement disorders has not been sufficiently addressed. This observational study fills a knowledge gap by reporting the nature of individuals utilizing a nurse-administered telephone service and the reasons for and the outcomes of calls. METHOD: Consecutive calls received by the clinic for a 12-month duration were recorded. A sample of 312 calls from 132 patient charts was analyzed. Variables for analysis and coding schema were determined a-priori and included demographic information as well as information around the reasons for and outcomes of calls. The narratives of documented calls were reviewed retrospectively and responses coded for analysis by a separate researcher. Data was analyzed using descriptive statistical methods. RESULT: Patients made the majority of calls (49%). 27% of calls related to worsening symptoms and another 35% of calls related to medication issues or renewals. The mean call duration was 15.93 minutes. The majority of calls were received mid-way between clinic visits (M = 89.24 days). The nurse resolved 84% of calls independently. The mean number of calls per patient was 2.93. Issues reported by patients were resolved (approximately 90%) without need for follow-up emergency, family, or subspecialty clinic visits. CONCLUSION: The results underscore the complexity of medical issues in a movement disorders population. The current study provides support for a nurse-administered telephone follow-up program in movement disorders.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".