Sertraline and Fluoxetine Treatment of Obsessive-Compulsive Disorder: Results of a Double-Blind, 6-Month Treatment Study
Bibliographic record
Abstract
The purpose of this study was to evaluate the comparative efficacy and tolerability of sertraline and fluoxetine in the treatment of obsessivecompulsive disorder (OCD). Outpatients meeting DSM-IV criteria for OCD, with a Yale-Brown Obsessive-Compulsive (Y-BOCS) total score ≥ 17, an NIMH Global Obsessive-Compulsive (NIMH-OC) scale score ≥ 7, and a CGI-Severity score ≥ 4 were randomized to 24 weeks of double-blind treatment with sertraline (N = 77) or fluoxetine (N = 73). Primary efficacy measures consisted of the Y-BOCS, the NIMH-OC scale, and the CGI-Severity (CGI-S) and Improvement (CGI-I) scales. Equivalent and significant (p < 0.001) improvement was found at week 24 in Y-BOCS and NIMH-OC scale scores for sertraline and fluoxetine. After 12 weeks, 49.2% of patients on sertraline were rated on the CGI-S scale as being mildly ill or not ill compared to 24.6% on fluoxetine (p < 0.01). A Cox analysis found patients on sertraline to have a statistically nonsignificant 42% greater likelihood of achieving a response by week 12 (CGI-I, much or very much improved; 95% CI, 0.85, 2.38;p = 0.18). Sertraline treatment also resulted in a higher proportion of remissions than fluoxetine (defined as a CGI-I ≤ 2 and a Y-BOCS score ≤ 11), both at week 12 (20% vs. 8%; χ2, 3.95;df 1;p = 0.047) and week 24 (36% vs. 22%; χ2, 3.18;df, 1;p = 0.075). Both medications were welltolerated and demonstrated significant efficacy in the treatment of outpatients with moderate to severe OCD with the subjects treated with sertraline showing a greater likelihood of remission as well as an earlier improvement on some but not all efficacy measures
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".