Risk of depression and common comorbid mental disorders in persons with malaria: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Aim To estimate the pooled prevalence and incidence of depression and its common comorbid mental disorders in persons with a malarial infection and its neurological complications Method We will conduct a systematic review and meta-analysis of studies published between January 1, 1960 and January 1, 2020, reporting the prevalence or incidence of common mental disorders and the risks in people with malaria. We will search the following databases: PubMed (MEDLINE), Scopus, OVID (HEALTH STAR), OVID (MEDLINE) and Joana Briggs Institute EBF Database. No age, geographical location, study-design or language limits will be applied. If multiple languages were used to describe and publish the same data, the English version was selected. This protocol was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. Three reviewers (MC, YP, YY) will independently screen citations, abstracts and will identify full-text articles for inclusion, extract data and appraise the quality and bias of included studies. Discrepancies will be resolved by consensus or consultation with a fourth researcher (AS). Risk of bias of included studies will be assessed by the Newcastle-Ottawa Quality Assessment Scale. The primary outcomes will be the overall prevalence or incidence of depression and common comorbid mental disorders (CCMDs) in persons with malaria. We will use the random-effects model with a logit transformation of proportions for the pooling of studies. We will assess the between-study heterogeneity using I2 statistics, and Cochrane’s Q statistic (significance level < 0.05). We will perform subgroup meta-analyses to investigate geographical differences in mental disorders and risks of different mental disorders. We will conduct a meta-regression analysis, using study level median age, race and gender proportions, the proportion of study population with malaria, and percentage of the study population with a diagnosis of common mental disorders. We will report absolute differences in the overall probability of common mental disorders. The Egger’s test and funnel plots will be used to assess publication bias.
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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 | 0.091 | 0.166 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".