Economic evaluation of a one-time screening for bipolar disorder using the EarlyDetect model in primary care in Canada
Bibliographic record
Abstract
BACKGROUND: EarlyDetect is a digital self-administered screening tool that has been recently developed to help clinicians diagnose bipolar disorder (BD). Improved diagnosis of this mental health condition is expected to reduce health care utilization and cost burden. We present an early health technology assessment (eHTA) threshold analysis exploring the potential value of EarlyDetect. METHODS: This study is not a traditional cost-effectiveness analysis but an exploratory eHTA using a threshold analysis approach. A decision analytic model was developed to evaluate the five-year impact of EarlyDetect compared to Mood Disorder Questionnaire (MDQ) or no screening on a cohort of individuals presenting to a primary care physician with a new depressive episode. Model parameters were derived from published literature or expert opinion. The percentage of individuals misdiagnosed was estimated. Total five-year health care costs were calculated from the Canadian public health care payer perspective and reported in Canadian dollars. Results were presented across the range of cut-off values. RESULTS: Over a five-year period, EarlyDetect decreased health care costs by up to $6868 and decreased the number of misdiagnosed individuals by 3.5 % when compared to no-screening. For MDQ, health care costs were reduced by up to $4690 with a reduction of misdiagnosed individuals of 2.3 %. CONCLUSIONS: BD screening with EarlyDetect and MDQ may lower health care costs and decrease the number of individuals misdiagnosed compared to no screening. These benefits may be greater for EarlyDetect than with MDQ. This analysis has limitations and includes several assumptions. The results should be interpreted with caution.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".