Influenza: clinical aspects, diagnosis, and treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the clinico-epidemiological aspects of influenza in the context of the Coronavirus Disease 2019 (COVID-19) pandemic; the recent advances in point-of-care molecular diagnostics and co-detection of influenza and coronaviruses, and the development of new influenza therapeutics. RECENT FINDINGS: Rates of influenza have declined globally since the 2020-2021 season; waning population immunity and uncertainty in vaccine strains could pose a risk in its significant resurgence, especially where pandemic public health interventions start being lifted. As symptoms are similar for influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, accurate, rapid diagnostics are needed to guide management. In addition to neuraminidase inhibitors, newer class of antivirals including polymerase inhibitors show promise in treating influenza infections in adults, children, and high-risk individuals. SUMMARY: This review summarizes the most recent data on rapid molecular diagnostics, including point-of-care tests and co-detection of influenza and SARS-CoV-2 viruses. The implications to inform clinical and infection control practices, and detection of antiviral resistance are discussed. The latest clinical trial data on neuraminidase inhibitors and polymerase inhibitors, their efficacy, limitations, and resistance concerns are reviewed.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".