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Antiviral Medications for Treatment of Nonsevere Influenza

2025· review· en· W4406308323 on OpenAlexaff
Ya Gao, Yunli Zhao, Ming Liu, Shuyue Luo, Yamin Chen, Xiaoyan Chen, Qingyong Zheng, Jianguo Xu, Yanjiao Shen, Wanyu Zhao, Zhifan Li, Sha Huang, Jie Huang, Jinhui Tian, Gordon Guyatt, Qiukui Hao

Bibliographic record

VenueJAMA Internal Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineAntiviral treatmentVirologyIntensive care medicineVirus

Abstract

fetched live from OpenAlex

Importance: The optimal antiviral drug for treatment of nonsevere influenza remains unclear. Objective: To compare effects of antiviral drugs for treating nonsevere influenza. Data Sources: MEDLINE, Embase, CENTRAL, CINAHL, Global Health, Epistemonikos, and ClinicalTrials.gov were searched from database inception to September 20, 2023. Study Selection: Randomized clinical trials comparing direct-acting influenza antiviral drugs to placebo, standard care, or another antiviral drug for treating people with nonsevere influenza. Data Extraction and Synthesis: Paired reviewers independently performed data extraction and risk of bias assessment. A frequentist network meta-analysis was performed to summarize the evidence and the certainty of evidence was evaluated using the GRADE approach. Main Outcomes and Measures: Mortality, admission to hospital, admission to the intensive care unit, duration of hospitalization, time to alleviation of symptoms, emergence of resistance, and adverse events. Results: Overall, 73 trials with 34 332 participants proved eligible. Compared with standard care or placebo, all antiviral drugs had little or no effect on mortality for low-risk patients and high-risk patients (all high certainty). All antiviral drugs (no data for peramivir and amantadine) had little or no effect on hospital admission for low-risk patients (high certainty). For hospital admission in high-risk patients, oseltamivir (risk difference [RD], -0.4%; 95% CI, -1.0 to 0.4; high certainty) had little or no effect and baloxavir may have reduced risk (RD, -1.6%; 95% CI, -2.0 to 0.4; low certainty); all other drugs may have had little or uncertain effect. For time to alleviation of symptoms, baloxavir probably reduced symptom duration (mean difference [MD], -1.02 days; 95% CI, -1.41 to -0.63; moderate certainty); umifenovir may have reduced symptom duration (MD, -1.10 days; 95% CI, -1.57 to -0.63; low certainty); oseltamivir probably had no important effect (MD, -0.75 days; 95% CI, -0.93 to -0.57; moderate certainty). For adverse events related to treatment, baloxavir (RD, -3.2%; 95% CI, -5.2 to -0.6; high certainty) had few or no adverse events; oseltamivir (RD, 2.8%; 95% CI, 1.2 to 4.8; moderate certainty) probably increased adverse events. Conclusions and Relevance: This systematic review and meta-analysis found that baloxavir probably reduced risk of hospital admission for high-risk patients and may reduce time to alleviation of symptoms, without increasing adverse events related to treatment in patients with nonsevere influenza. All other antiviral drugs either probably have little or no effect, or uncertain effects on patient-important outcomes.

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)0.012
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.177
GPT teacher head0.509
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations34
Published2025
Admission routes1
Has abstractyes

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