Myotonic Dystrophy and Huntington’s Disease Care: “We Like to Think We’re Making a Difference”
Bibliographic record
Abstract
BACKGROUND: Patient-centered care for individuals with myotonic dystrophy (DM1) and Huntington's disease (HD)-chronic, progressive, and life-limiting neurological conditions-may be challenged by patients' cognitive and behavioral impairments. However, no research has explored health care providers' (HCPs') perspectives about patient-centered care provision for these patients along their disease trajectory. METHODS: Constructivist grounded theory informed the iterative data collection and analysis process. Eleven DM1 or HD HCPs participated in semistructured interviews, and three stages of coding were used to analyze their interview transcripts. Codes were collapsed into themes and categories. RESULTS: Three categories including an evolving care approach, fluid roles, and making a difference were identified. Participants described that their clinical care approach evolved depending on the patient's disease stage and caregivers' degree of involvement. HCPs described that their main goal was to provide hope to patients and caregivers through medical management, crisis prevention, support, and advocacy. Despite the lack of curative treatments, HCPs perceived that patients benefited from ongoing clinical care provided by proactive clinicians. CONCLUSIONS: Providing care for individuals with DM1 and HD is a balancing act. HCPs must strike a balance between (1) the frustrations and rewards of patient-centered care provision, (2) addressing symptoms and preventing and managing crises while focusing on patients' and caregivers' quality of life concerns, and (3) advocating for patients while addressing caregivers' needs. This raises important questions: Is patient-centered care possible for patients with cognitive decline? Does chronic neurological care need to evolve to better address patients' and caregivers' complex needs?
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".