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Record W4398241152 · doi:10.3389/fsci.2024.1236919

Standing the test of COVID-19: charting the new frontiers of medicine

2024· article· en· W4398241152 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Science · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Test (biology)2019-20 coronavirus outbreakVirologyMedicineInternal medicineBiologyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic accelerated research and innovation across numerous fields of medicine. It emphasized how disease concepts must reflect dynamic and heterogeneous interrelationships between physical characteristics, genetics, co-morbidities, environmental exposures, and socioeconomic determinants of health throughout life. This article explores how scientists and other stakeholders must collaborate in novel, interdisciplinary ways at these new frontiers of medicine, focusing on communicable diseases, precision/personalized medicine, systems medicine, and data science. The pandemic highlighted the critical protective role of vaccines against current and emerging threats. Radical efficiency gains in vaccine development (through mRNA technologies, public and private investment, and regulatory measures) must be leveraged in the future together with continued innovation in the area of monoclonal antibodies, novel antimicrobials, and multisectoral, international action against communicable diseases. Inter-individual heterogeneity in the pathophysiology of COVID-19 prompted the development of targeted therapeutics. Beyond COVID-19, medicine will become increasingly personalized via advanced omics-based technologies and systems biology—for example targeting the role of the gut microbiome and specific mechanisms underlying immunoinflammatory diseases and genetic conditions. Modeling proved critical to strengthening risk assessment and supporting COVID-19 decision-making. Advanced computational analytics and artificial intelligence (AI) may help integrate epidemic modeling, clinical features, genomics, immune factors, microbiome data, and other anthropometric measures into a “systems medicine” approach. The pandemic also accelerated digital medicine, giving telehealth and digital therapeutics critical roles in health system resilience and patient care. New research methods employed during COVID-19, including decentralized trials, could benefit evidence generation and decision-making more widely. In conclusion, the future of medicine will be shaped by interdisciplinary multistakeholder collaborations that address complex molecular, clinical, and social interrelationships, fostering precision medicine while improving public health. Open science, innovative partnerships, and patient-centricity will be key to success.

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.

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)0.023
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.494
GPT teacher head0.564
Teacher spread0.070 · 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