Evaluation of the Quality of Guidelines for the Treatment of Symptoms of Dengue, Zika, and Chikungunya and Management of the Aedes aegypti Mosquito
Bibliographic record
Abstract
1. INTRODUCTION Arboviruses such as Dengue, Zika, and Chikungunya are caused by the DENV, ZIKV, and CHIKV viruses, respectively[1-3]. Symptoms include fever, skin rash, nausea, vomiting, conjunctivitis, easy bleeding, body pain, and hematological changes. Severe complications like Guillain-Barré syndrome, neuropathies, myelitis, hemorrhage, fetal malformations, miscarriages, premature births, and death can occur[2-4]. The treatment for these diseases is symptomatic, generally involving the use of paracetamol or dipyrone, hydration, and rest[5]. Transmission occurs mainly through mosquitoes of the Aedes genus in tropical and subtropical regions, including around 128 countries[6]. In Brazil, the Aedes aegypti mosquito is the primary vector, and mosquito control is the most effective measure to curb the spread of these arboviruses, mainly by eliminating standing water sites necessary for the hatching of eggs[3, 5]. To guide effective public policies in disease treatment and vector control, it is crucial to gather relevant scientific evidence and develop guiding documents known as guidelines[7]. Health guidelines are important tools for improving the quality of healthcare services[8-10] and can be divided into clinical practice guidelines (CPGs) and health system guidelines (HS). CPGs provide recommendations based on systematic reviews to optimize patient care[11, 12], while health system guidelines offer solutions for policy development in health systems[13-15]. To ensure the quality in the development and purpose of these guidelines, tools have been developed since the early 1990s. Today, the most widely used are from the AGREE collaboration: AGREE II[16]– methodological quality of clinical guidelines; AGREE REX[17]– excellence of recommendations; and AGREE HS[18]– methodological quality of health system guidelines. 1.1 JUSTIFICATION According to the latest epidemiological bulletin from the Ministry of Health, covering the first 7 epidemiological weeks of 2024, the number of probable dengue cases has tripled compared to the same period in 2023, primarily affecting the Southeast, Midwest, South, and North regions of Brazil, with around 700,000 Brazilians affected by the disease. For the same period, approximately 40% of the Chikungunya cases from 2023 have already been recorded (about 150,000)[19]. Thus, considering the dengue epidemic, the large number of Chikungunya cases, and a likely increase in Zika cases, effective sanitary measures are needed to combat these arboviruses, either through mosquito management or symptom treatment, justifying the comparison and verification of the methodological quality and recommendations of existing guidelines. 1.2 OBJECTIVES 1.2.1 General Objective To evaluate the methodological quality and recommendations for the treatment of symptoms of Dengue, Zika, and Chikungunya and for the management of Aedes aegypti mosquitoes. 1.2.2 Specific Objectives Evaluate and compare the methodological quality of the guidelines. Evaluate and compare treatment recommendations for diseases and mosquito management. 1. Guzman, M.G., et al., Dengue: a continuing global threat. Nat Rev Microbiol, 2010. 8(12 Suppl): p. S7-16. 2. Krauer, F., et al., Zika Virus Infection as a Cause of Congenital Brain Abnormalities and Guillain-Barré Syndrome: Systematic Review. PLoS Med, 2017. 14(1): p. e1002203. 3. Weaver, S.C. and W.K. Reisen, Present and future arboviral threats. Antiviral Res, 2010. 85(2): p. 328-45. 4. de Araújo, T.V.B., et al., Association between microcephaly, Zika virus infection, and other risk factors in Brazil: final report of a case-control study. Lancet Infect Dis, 2018. 18(3): p. 328-336. 5. Soni, S., et al., Dengue, Chikungunya, and Zika: The Causes and Threats of Emerging and Re-emerging Arboviral Diseases. Cureus, 2023. 15(7): p. e41717. 6. Brady, O.J., et al., Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis, 2012. 6(8): p. e1760. 7. Schunemann, H.J., et al., Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise. Canadian Medical Association Journal, 2014. 186(3): p. E123-E142. 8. Woolf, S.H., Practice guidelines: a new reality in medicine. I. Recent developments. Arch Intern Med, 1990. 150(9): p. 1811-8. 9. Woolf, S.H., et al., Clinical guidelines: potential benefits, limitations, and harms of clinical guidelines. BMJ, 1999. 318(7182): p. 527-30. 10. Harris, R.P., et al., Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med, 2001. 20(3 Suppl): p. 21-35. 11. IOM, Institute of Medicine. Committee on Standards for Developing Trustworthy Clinical Practice, Guidelines. Clinical Practice Guidelines We Can Trust., ed. R. Graham, et al. 2011, Washington (DC): National Academies Press. 12. IOM, Institute of Medicine. Clinical Practice Guidelines: Directions for a New Program, ed. M.J. Field and K.N. Lohr. 1990, Washington, DC: The National Academies Press. 168. 13. Ako-Arrey, D.E., et al., Health systems guidance appraisal--a critical interpretive synthesis. Implement Sci, 2016. 11(1): p. 9. 14. Ako-Arrey, D.E., et al., Health system guidance appraisal--concept evaluation and usability testing. Implement Sci, 2016. 11: p. 3. 15. Brouwers, M.C., et al., Validity and usability testing of a health systems guidance appraisal tool, the AGREE-HS. Health Res Policy Syst, 2018. 16(1): p. 51. 16. Brouwers, M.C., et al., AGREE II: advancing guideline development, reporting and evaluation in health care. CMAJ, 2010. 182(18): p. E839-42. 17. Florez, I.D., et al., Assessment of the quality of recommendations from 161 clinical practice guidelines using the Appraisal of Guidelines for Research and Evaluation–Recommendations Excellence (AGREE-REX) instrument shows there is room for improvement. Implementation Science, 2020. 15(1): p. 79. 18. Brouwers, M.C., et al., Assessment of health systems guidance using the Appraisal of Guidelines for Research and Evaluation - Health Systems (AGREE-HS) instrument. Health Policy, 2019. 123(7): p. 646-651.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".