Fluoxetine in the Treatment of Premenstrual Dysphoric Disorder
Bibliographic record
Abstract
OBJECTIVE: To examine the role of fluoxetine in the treatment of premenstrual dysphoric disorder (PMDD). DATA SOURCES: Search strategy included MEDLINE (1966-February 2002), Embase (1988-February 2002), HealthStar (1975-December 2000), Current Contents (1996-November 2001), and Copernic (November 2001). Search terms included fluoxetine, premenstrual dysphoric disorder, PMDD, late luteal-phase dysphoric disorder, and severe premenstrual syndrome. STUDY SELECTION: English-language human studies were selected and evaluated based on quality of evidence. DATA SYNTHESIS: Eight prospective trials (3 double-blind, placebo-controlled, crossover; 3 double-blind, randomized, controlled; 2 open-label), 1 case series, and 1 meta-analysis were identified. Although 6 of the studies involved small sample sizes (n < 50), all found fluoxetine to be effective in the treatment of PMDD. CONCLUSIONS: Despite limited data, fluoxetine 20 mg/d appears to be effective in the treatment of PMDD. However, adverse effects, particularly headaches and sexual dysfunction, are possible. Given the long half-life of fluoxetine and the short duration of PMDD symptoms per cycle, larger, well-designed clinical trials evaluating intermittent dosing for only 1 week or a few doses need to be performed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".