Evaluation of the Impact of Integrated Care and Self-Management After Deep Brain Stimulation in Parkinson’s Disease
Bibliographic record
Abstract
Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder with a myriad of motor and non-motor symptoms. Although deep brain stimulation (DBS) has a dramatic impact in the lives of people with PD, care delivery remains complex. There is a lack of evidence on the implementation and role of integrated care and self-management support in people with PD and chronic DBS. Objective: To evaluate care needs, implementation and impact of a pragmatic network for PD care, the Integrated Parkinson Care Network (IPCN) in people with PD and chronic DBS. Methods: This is a subgroup analyses of a 6-month, pre-post design, single-centre, phase 2 study to assess a patient-centred care model based on integrated care, self-management support in PD (IPCN), focusing on those participants with chronic DBS. Results: We included 22 people with PD and chronic DBS (median time since DBS - 30 months). The mean age was 63.9 (7.6) years and mean disease duration was 15.2 (6.9) years. The top three care priorities were speech (54.5%), mobility (40.9%) and mood (31.8%). After the IPCN program, there was a positive change in the perception of support for chronic care (Patient Assessment of Chronic Illness Care +: -0.84; 95% CI: -1.2 to -0.5) and self-management (5As: -0.77; 95% CI: -1.1 to -0.4), along with quality of life (PDQ8 : 7.1, 95% CI:1.8 -12.4). Conclusion: The IPCN is a care delivery model that addresses specific care needs of people with PD and chronic DBS. The current study showed its feasibility and warrants further evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".