Longitudinal study to assess antidepressant treatment patterns and outcomes in individuals with depression in the general population
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is largely managed in primary care, but physicians vary widely in their understanding of symptoms and treatments. This study aims to better understand the evolution of depression from initial diagnosis over a 3-year period. METHODS: This was a noninterventional, retrospective, longitudinal study, with 2 waves of participant interviews approximately 3 years apart. Phone interviews were conducted using the hybrid artificial intelligence (AI) Sleep-EVAL system, an AI-driven diagnostic deep learning tool. Participants were noninstitutionalized adults representative of the general population in 8 US states. Diagnosis was confirmed according to the DSM-5 using the Sleep-EVAL System. RESULTS: 10,931 participants completed Wave 1 and 2 (W1, W2) interviews. The prevalence of MDD, including partial and complete remission, was 13.4 % and 19.6 % in W1 and W2, respectively. About 42 % of MDD participants at W1 continued to report depressive symptoms at W2. Approximately half of antidepressant (AD) users in W1 were moderately to completely dissatisfied with their treatment; 29.6 % changed their AD for a different one, with 16.4 % switching from one SSRI to another between W1 and W2. Primary care physicians were the top AD prescribers, both in W1 (45.7 %) and W2 (59%), respectively. LIMITATIONS: Data collected relied on self-reporting by participants. As such, the interpretation of the data may be limited. CONCLUSIONS: Depression affects a sizeable portion of the US population. Dissatisfaction with treatment, frequent switching of ADs, and changing care providers are associated with low rates of remission. Residual symptoms remain a challenge that future research must address.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".