Protocol of a systematic review and network meta-analysis for the prevention and treatment of perinatal depression
Bibliographic record
Abstract
INTRODUCTION: Perinatal depression is common and can often lead to adverse health outcomes for mother and child. Multiple pharmacological and non-pharmacological treatments have been evaluated against usual care or placebo controls in meta-analyses for preventing and treating perinatal depression compared. It is not yet established which of these candidate treatments might be the optimal approach for prevention or treatment. METHODS AND ANALYSIS: A systematic review and Bayesian network meta-analyses will be conducted. Eight electronic databases shall be searched for randomised controlled trials that have evaluated the effectiveness of treatments for prevention and/or treatment of perinatal depression. Screening of articles shall be conducted by two reviewers independently. One network meta-analysis shall evaluate the effectiveness of interventions in preventing depression during the perinatal period. A second network meta-analysis shall compare the effectiveness of treatments for depression symptoms in women with perinatal depression. Bayesian 95% credible intervals shall be used to estimate the pooled mean effect size of each treatment, and surface under cumulative ranking area will be used to rank the treatments' effectiveness. ETHICS AND DISSEMINATION: We shall report our findings so that healthcare providers can make informed decisions on what might be the optimal approach for addressing perinatal depression to prevent cases and improve outcomes in those suffering from depression through knowledge exchange workshops, international conference presentations and journal article publications. PROSPERO REGISTRATION NUMBER: CRD42020200081.
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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 | 0.128 | 0.218 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.026 | 0.024 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.103 | 0.014 |
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".