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Record W4313815814 · doi:10.1503/cmaj.1096031

What RSV interventions are in the research pipeline?

2023· article· en· W4313815814 on OpenAlexvenueno aff
Diana Duong

Bibliographic record

VenueCanadian Medical Association Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicinePandemicPsychological interventionRespiratory illnessSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Immunity2019-20 coronavirus outbreakVirusVirologyIntensive care medicinePediatricsRespiratory systemImmunologyInternal medicineImmune systemDiseaseInfectious disease (medical specialty)Nursing

Abstract

fetched live from OpenAlex

Reduced immunity in the wake of the COVID-19 pandemic has contributed to large surges in respiratory syncytial virus (RSV) infections this year, overwhelming pediatric hospitals. While RSV mostly causes mild, cold-like symptoms in older children and adults, it causes serious illness requiring

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)0.051
metaresearch head score (Gemma)0.156
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.156
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0030.003
Science and technology studies0.0020.004
Scholarly communication0.0130.020
Open science0.0040.004
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0510.010

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 head0.162
GPT teacher head0.464
Teacher spread0.302 · 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 designNot applicable
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

Citations4
Published2023
Admission routes1
Has abstractyes

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