Impact of fatigue on outcome of selective serotonin reuptake inhibitor treatment: secondary analysis of STAR*D
Bibliographic record
Abstract
OBJECTIVE: To explore relationships between baseline and changes in fatigue during treatment with outcomes in patients with major depressive disorder (MDD) receiving citalopram monotherapy. RESEARCH DESIGN AND METHODS: Secondary analyses of data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Level 1 treatment phase (≤14 weeks citalopram monotherapy). Fatigue was assessed with item 14 on energy level from the 16-item Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16; scored 0-3: 0 = no fatigue, 3 = maximal fatigue); prospective fatigue: assessment of fatigue at Level 1 entry and exit (no fatigue, treatment-emergent fatigue, remitted fatigue, or residual fatigue). CLINICAL TRIAL REGISTRATION: Http://clinicaltrials.gov, NCT00021528. MAIN OUTCOME MEASURES: Remission of depressive symptoms (17-item Hamilton Rating Scale for Depression ≤7 or QIDS-SR16 ≤5); Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form; Short-Form Health Survey Mental and Physical subscales; and Work and Social Adjustment Scale (WSAS). RESULTS: At baseline, of 2868 patients included in the analyses, 5.5% had a QIDS-SR16 item 14 score of 0; 22.9%, a score of 1; 53.6%, a score of 2; and 18.0%, a score of 3. During Level 1 treatment, 3.5% of patients had no prospective fatigue, 2.1% had treatment-emergent fatigue, 33.6% had fatigue remitting during treatment, and 60.8% had residual fatigue. Female gender, unemployment, fewer years of education, and lower monthly income were significantly associated with higher rates of baseline fatigue (all P < 0.0001). Higher levels of baseline or prospective fatigue were associated with reduced likelihood of remission, decreased overall satisfaction (P < 0.0001), and reduced mental and physical function at outcome (P ≤ 0.05). Patients with higher baseline or prospective fatigue reported higher WSAS total scores (P < 0.0001), indicative of more severe functional impairment. CONCLUSIONS: Lower baseline fatigue and remission of fatigue during antidepressant treatment in patients with MDD are associated with higher rates of remission of depressive symptoms and better function and quality of life. Study limitations include use of the STAR*D Level 1 sample (citalopram as only antidepressant), use of a proxy measure of energy/fatigue (item 14 from the QIDS-SR16), and the secondary post-hoc analysis design.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".