The efficacy and safety of intermittent preventive treatment with sulphadoxine-pyrimethamine vs artemisinin-based drugs for malaria: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Malaria is one of the most serious global problems. The objective of this study is to assess whether intermittent preventive treatment (IPT) using artemisinin-based combination therapies (ACTs) was a promising alternative to IPT with sulphadoxine-pyrimethamine (IPT-SP). METHODS: We searched the following sources up to 12 August 2020: PubMed, The Cochrane Library, Embase, Web of Science, CNKI, CBM, VIP and WanFang Database from inception. The randomized controlled trials comparing SP with ACTs for malaria were included. Data were pooled using Stata.14 software. We performed subgroup analysis based on the different types of ACTs groups and participants. RESULTS: A total of 13 studies comprising 5180 people were included. The meta-analysis showed that ACTs had the lower risk of number of any parasitemia (RR=0.46; 95% CI 0.22 to 0.96, p=0.039; I2=90.50%, p<0.001), early treatment failure (RR=0.17; 95% CI 0.06 to 0.48, p<0.001; I2=66.60%, p=0.011) and late treatment failure (RR=0.34; 95% CI 0.13 to 0.92, p<0.001; I2=87.80%, p<0.001) compared with SP. There was no significant difference in adequate clinical response, average hemoglobin and adverse neonatal outcomes. CONCLUSION: Combinations with ACTs appear promising as suitable alternatives for IPT-SP.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.051 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".