Development of a New Care Pathway for Depression and Anxiety in Adult‐Onset Isolated Dystonia
Bibliographic record
Abstract
Background: Recently, we identified barriers and facilitators to the screening and treatment of depressive and anxiety symptoms in adult-onset isolated dystonia (AOID). These symptoms are common, functionally impairing, and often underdetected and undertreated. Objectives: To develop a care pathway for mood symptoms in AOID. Methods: We used a multistep modified Delphi approach to seek consensus among healthcare professionals with experience of AOID on the screening, diagnosis, and treatment of mood symptoms. A combination of face-to-face meetings and online surveys was performed from 2019 to 2020. We created the survey and then reviewed with stakeholders before 2 rounds of Delphi surveys, all of which was finally reviewed in a consensus meeting. A purposive sample of 41 expert stakeholders from 4 Canadian provinces, including neurologists, nurses, psychiatrists, psychologists, and family physicians, was identified by the research team. Results: The Delphi process led to consensus on 12 statements that operationalized a pathway of care to screen for and manage depression and anxiety in people with AOID. Key actions of the pathway included yearly screening with self-rated instruments, multidisciplinary involvement in management involving local networks of providers coordinated by movement disorders neurologists, and access to educational resources. The Delphi panel indicated the 2 core steps as the documentation of the most recent screening outcome and the documentation of a management plan for patients who were positive at the last screening. Conclusions: This new care pathway represents a potentially useful intervention that can be used to build an integrated model of care for AOID.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".