The real-world health resource use and costs of misdiagnosing bipolar I disorder
Bibliographic record
Abstract
BACKGROUND: Misdiagnosis of bipolar I disorder (BP-I) as major depressive disorder (MDD) is common. This study evaluated healthcare resource utilization (HRU) and costs among BP-I patients who were initially misdiagnosed with MDD (misdiagnosed BP-I cohort) versus patients diagnosed with BP-I without a known prior MDD diagnosis (BP-I only cohort). METHODS: Data from IBM® MarketScan® Research Databases were used. The index date was the first MDD diagnosis for misdiagnosed patients or first BP-I diagnosis for BP-I only patients. Inverse probability of treatment weighting was used to balance baseline characteristics between cohorts. All-cause and mental health (MH)-related HRU and costs were compared between weighted cohorts using rate ratios (RRs) and mean cost differences, respectively. Outcomes were reported per patient-year (PPY). Confidence intervals and P-values were calculated using non-parametric bootstrap procedures. RESULTS: Overall, 14,729 misdiagnosed BP-I and 16,072 BP-I only patients met criteria. Baseline characteristics were balanced across weighted cohorts. Misdiagnosed BP-I patients had significantly higher rates of hospitalizations, emergency room visits, and outpatient visits than BP-I only patients during follow-up (all-cause RRs: 1.94, 1.33, and 1.38, respectively, all P < .001; MH-related RRs: 2.19, 1.77, and 1.77, respectively, all P < .001). Similarly, misdiagnosed BP-I patients incurred significantly higher total healthcare costs PPY over follow-up (all-cause: $21,202 vs $14,661, cost difference = $6541; MH-related: $12,901 vs $6749, cost difference = $6152; both P < .001). Cost differences were even higher during the first year (all-cause = $7146; MH-related = $6619; both P < .001). LIMITATIONS: Claims database (e.g., coding inaccuracies); generalizability to uninsured patients. CONCLUSIONS: The prompt and correct diagnosis of BP-I may significantly reduce HRU and costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".