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Record W2123348631 · doi:10.1097/ccm.0b013e3181cd7bb2

Laboratory diagnosis of 2009 H1N1 influenza A virus

2010· article· en· W2123348631 on OpenAlexaff
Andrea K. Boggild, Allison McGeer

Bibliographic record

VenueCritical Care Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicinePandemicH1N1 influenzaH1n1 pandemicVirusInfluenza A virusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Intensive care medicineVirologyDiagnostic testRelevance (law)2019-20 coronavirus outbreakPathologyEmergency medicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

The emergence of 2009 pandemic influenza H1N1 has necessarily led to the rapid evolution of sensitive, specific, and high-throughput molecular diagnostic assays for this virus at the same time that clinical laboratories attempt to cope with increasing demands in the setting of resource limitations. This situation has given rise to testing algorithms focusing on priority, clinical relevance, and appropriate surveillance. We describe the current state of understanding around diagnostic testing and laboratory detection of 2009 H1N1 influenza A virus.

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)0.004
metaresearch head score (Gemma)0.009
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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.063
GPT teacher head0.430
Teacher spread0.367 · 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
GenreMethods

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

Citations18
Published2010
Admission routes1
Has abstractyes

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