Use of a point‐of‐care web‐based application to enhance adherence to the CANMAT and ISBD 2018 guidelines for the management of bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: While clinical guidelines exist for the management of bipolar disorder (BD), there are significant challenges to their widespread dissemination and implementation in clinical practice. The Canadian Network of Mood and Anxiety Treatment Improving Patient Care and Outcomes in the Treatment of Bipolar Disorder (C-IMPACT BD) web-based application was developed for use at the point-of-care to improve adherence to guidelines for evidence-based pharmacological management of BD. METHODS: C-IMPACT BD uses a point-of-care practice assessment which, via adaptive questioning of patient-specific information, text/video descriptions of the guidelines, and pop-up prompts delivers personalized, evidence-based treatment recommendations for patients with BD. In order to inform quality improvement of the newly developed tool, a sample of Canadian physicians were invited to use the application and record its influence on their prescribing behavior. RESULTS: Of 375 patients with bipolar I (BD-I) or bipolar II (BD-II) disorder for whom a point-of-care practice assessment was completed, a change in therapy was considered for 225 (60.0%). Prior to completing the assessment, 59.6% of these patients were receiving first-line therapy recommended for their phase of illness. Following the assessment, the overall number of patients for whom a first-line recommended therapy was being considered increased significantly to 76.9% (p = 0.0001). CONCLUSIONS: Outcomes suggest that the C-IMPACT BD web-based application has the potential to improve physician adherence to clinical treatment guidelines. Formal research investigations are warranted to explore the impact of this tool on physician prescribing behavior and patient outcomes.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".