The Brain Medicine Clinic: two cases highlighting the advantages of integrative care
Bibliographic record
Abstract
BACKGROUND: Current assessment and management models often do not adequately address the many aspects of managing complex brain disorders involving disordered affect, behaviour and cognition (ABC). A more collaborative model of care, where several specialties can jointly assess and manage patients with complex brain disorders, is gaining attention. AIMS: In this case report, we present two cases that highlight the benefits of the 'brain medicine' clinical model. METHOD: The Brain Medicine Clinic employs an integrated clinical model in which psychiatrists and neurologists provide integrated interdisciplinary assessments of patients with complex brain disorders, leading to comprehensive assessment. We describe the clinical model and the trajectories of two patients with complex brain disorders seen in this clinic. In these case descriptions, we explain how the brain medicine clinical approach leads to an improved patient experience. RESULTS: The Brain Medicine Clinic assessments resulted in a neurobiopsychosocial formulation of symptoms and, consequently, holistic individualised treatment plans for two patients with complex brain disorders. This approach to patients' conditions emerges from the understanding that there are multifactorial causes of brain disorders at the social, cultural, psychological and biological level. CONCLUSIONS: Integrated interdisciplinary assessments allow for tailored treatment plans for individuals experiencing complex brain disorders, while creating efficiencies for the patient and the 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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".