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Record W6906494032 · doi:10.17605/osf.io/67sqv

Evaluation of the Quality of Guidelines for the Treatment of Symptoms of Dengue, Zika, and Chikungunya and Management of the Aedes aegypti Mosquito

2024· other· en· W6906494032 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsChikungunyaAedes aegyptiDiseaseQuality (philosophy)MalariaPublic healthTransmission (telecommunications)Health care

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.107
metaresearch head score (Gemma)0.296
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.296
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.007
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.213
GPT teacher head0.499
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueOpen Science FrameworkFrench-language works237,207