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Comparative Clinical Assessment and Risk Stratification of COVID-19 and Influenza Infections in Adults and Children: A Comprehensive Systematic Review and Meta-Analysis.

2024· review· en· W4402207532 on OpenAlexaboutno aff
Eman Shaban, Yavuz Yiğit, Mohamed Elgassim, Ahmed Shaban, Amira Shaban, Amin Wafer Ameen, Mohamed Abdurabu, Hany A Zaki

Bibliographic record

VenuePubMed · 2024
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Risk stratificationMeta-analysis2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicRisk assessmentStratification (seeds)Environmental healthIntensive care medicineVirologyInternal medicineInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

Objective: This study aimed to compare the clinical risks and outcomes of COVID-19 and influenza. Methods: The search for relevant articles was conducted using both a database search method and a manual search, which involved searching through the reference lists of articles related to the topic for additional studies. The Quality assessment was carried out using the Newcastle Ottawa tool, and the data analysis was conducted using the Review Manager Software (RevMan 5.4.1). Results: <0.00001). Conclusion: The findings suggested that COVID-19 was more severe than influenza. Therefore, "flu-like" symptoms should not be dismissed without a clear diagnosis, especially during the winter when influenza is more prevalent.

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)0.014
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.023
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.408
GPT teacher head0.551
Teacher spread0.143 · 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 designMeta-analysis
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

Citations0
Published2024
Admission routes1
Has abstractyes

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