FLUPHENAZINE (ORAL) vs placebo: Global state: Not improved or worsened ‐ medium term Dichotomised Montgomery‐Asberg Depression Rating Scale (MDRS)
It is very uncertain whether ORAL FLUPHENAZINE changes global state: Not improved or worsened ‐ medium termDichotomised… compared with PLACEBO (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
ORAL FLUPHENAZINE vs PLACEBO Global state: Not improved or worsened ‐ medium termDichotomised Montgomery‐Asberg Depression Rating Scale (MDRS) · patients with Schizophrenia It is very uncertain whether ORAL FLUPHENAZINE changes global state: Not improved or worsened ‐ medium termDichotomised… compared with PLACEBO (very low certainty). Fluphenazine (oral) versus placebo for schizophrenia (2018) · Cochrane Headline outcomes only | RR 1.12 [0.79, 1.58] | 1 study · 50 participants | Very low |
ORAL FLUPHENAZINE vs PLACEBO Global state: Not improved or worsened ‐ medium term Dichotomised Montgomery‐Asberg Depression Rating Scale (MDRS) · patients with Schizophrenia It is very uncertain whether ORAL FLUPHENAZINE changes global state: Not improved or worsened ‐ medium term Dichotomised… compared with PLACEBO (very low certainty). Fluphenazine (oral) versus placebo for schizophrenia (2013) · Cochrane Headline outcomes onlyearlier version CD006352.PUB2, superseded | RR 1.12 [0.79, 1.58] | 1 study · 50 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Fluphenazine (oral) versus placebo for schizophrenia 2018 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.