The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Trial: A Review
Bibliographic record
Abstract
OBJECTIVE: The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial is the largest open-label, pragmatic trial that has been undertaken to examine the treatment of major depressive disorder. At a cost of US$35 million over 6 years, STAR*D sought to test the effectiveness both of pharmacotherapy and of cognitive therapy, and to ascertain whether certain treatments are more optimal after one or more failed trials. METHOD: Patients (n = 2876) who presented to either a psychiatry or family practice setting seeking treatment for depression were included in the STAR*D analysis. In the 4 levels of STAR*D, patients were randomized to various treatment monotherapies, combinations, or augmentation strategies. The primary outcome was remission, based on the Hamilton Depression Rating Scale. Secondary outcomes were response, as measured by clinician and patient self-report as well as various measures of patients' level of function and (or) quality of life. RESULTS: Remission rates for treatment levels 1 to 2 and 3 to 4 were 18% to 30% and 7% to 25%, respectively. There was no difference in effectiveness between any treatments at any treatment level. Patients with longer index episodes, more concurrent psychiatric or general medical disorders, and (or) lower measures of baseline function were less likely to achieve remission. There were no major differences between outcomes in patients treated in primary, compared with specialist care, nor were there significant differences between depression rating scores obtained through clinician ratings, compared with self-report. CONCLUSION: Results of the STAR*D trial have shed important light on the effectiveness of current treatment strategies for patients with depression.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".