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Record W2107485527 · doi:10.1111/irv.12085

Antivirals for influenza: a summary of a systematic review and meta‐analysis of observational studies

2013· review· en· W2107485527 on OpenAlexafffund
Nancy Santesso, Jonathan C. Hsu, Reem A. Mustafa, Jan Brożek, Yao Long Chen, Jessica Hopkins, Adrienne Cheung, Liudmila Ivanova, Signe Flottorp, Ingvil Sæterdal, Arthur Wong, Jinhui Tian, Timothy M. Uyeki, Elie A. Akl, Pablo Alonso‐Coello, Fiona Smaill, Holger J. Schünemann

Bibliographic record

VenueInfluenza and Other Respiratory Viruses · 2013
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsRegional Municipality of NiagaraMcMaster University
FundersMcMaster UniversityWorld Health Organization
KeywordsObservational studyZanamivirMedicineRandomized controlled trialMeta-analysisOseltamivirIntensive care medicineSystematic reviewMEDLINEInternal medicineCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Despite the use of antivirals to treat patients with severe influenza, questions remain with respect to effects and safety. Although a recent systematic review has provided some indication of benefit, the analysis is limited by the quality of the available evidence from randomized controlled trials. To supplement the existing information, the authors conducted a systematic review of observational studies of antiviral treatment for influenza. This report summarises the findings of that review. Similar to the randomised trials, the confidence in the estimates of the effects for decision-making is low to very low primarily due to the risk of selection and publication bias in the observational studies. From these observational studies, the summary estimates suggest that oseltamivir may reduce mortality, hospitalisation and duration of symptoms compared with no treatment. Inhaled zanamivir may also reduce symptom duration and hospitalisations, but patients may experience more complications compared with no treatment. Earlier treatment with antivirals is generally associated with better outcomes than later treatment. Further high-quality evidence is needed to inform treatment guidelines because of the overall low to very low quality of evidence.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0160.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.777
GPT teacher head0.568
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2013
Admission routes2
Has abstractyes

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