Integrated Care in Neurology: The Current Landscape and Future Directions
Bibliographic record
Abstract
The rising burden of neurological disorders poses significant challenges to healthcare systems worldwide. There has been an increasing momentum to apply integrated approaches to the management of several chronic illnesses in order to address systemic healthcare challenges and improve the quality of care for patients. The aim of this paper is to provide a narrative review of the current landscape of integrated care in neurology. We identified a growing body of research from countries around the world applying a variety of integrated care models to the treatment of common neurological conditions. Based on our findings, we discuss opportunities for further study in this area. Finally, we discuss the future of integrated care in Canada, including unique geographic, historical, and economic considerations, and the role that integrated care may play in addressing challenges we face in our current healthcare system.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".