What Are the Implications of the STAR*D Trial for Primary Care?
Bibliographic record
Abstract
BACKGROUND: Although results of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial have been widely disseminated to mental health care providers, hitherto, primary care providers, who diagnose and manage most individuals with depressive syndromes, have had minimal exposure to the study's key findings. OBJECTIVE: We aim to provide translational implications of the STAR*D trial for primary care practitioners as well as for future research vistas. DATA SOURCES: A PubMed search was carried out with key search terms STAR*D and treatment-resistant depression found in articles published from 2001 through 2007. STUDY SELECTION: Articles reporting on the STAR*D outcomes at each sequence of treatment were the primary sources for review. DATA EXTRACTION: Results from the primary outcome measures at each sequential treatment were extracted and reviewed. Articles reporting variables affecting the probability of achieving remission were also selected. RESULTS: The STAR*D trial is the largest effectiveness study evaluating next-step therapies in real-world patients with major depressive disorder. The ecological validity of the study results are burnished by several methodological factors, including the enrollment of both publicly and privately insured patients, the recruitment of patients in primary and specialty care settings, the broad inclusion criteria, the use of pharmacologic and psychosocial (i.e., cognitive-behavioral therapy) treatment options, the use of measurement-based care, and the randomized clinical equipoise design. Taken together, remission rates of approximately 50% to 55% were reported after 2 sequential treatment interventions. A substantial percentage of individuals achieving remission do so after 6 weeks of treatment. The probabilities of achieving remission with third- and fourth-step therapy were considerably lower, i.e., ≤ 25%. The probabilities of relapse during continuation therapy increased as a function of number of treatment trials required to achieve remission. There is no evidence that individuals failing to achieve remission with a selective serotonin reuptake inhibitor (SSRI) have a greater probability of remitting with a separate class antidepressant versus an alternative SSRI. CONCLUSION: A window of therapeutic opportunity appears to exist insofar as acute remission rates in major depressive disorder are greatest with the first 2 sequential treatments. Taken together, measurement-based care affords the greatest probability that an individual will achieve remission. Despite optimal continuation treatment, relapse rates remain significant, underscoring the chronicity of depressive disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".