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Record W2020377168 · doi:10.1186/cc14044

Biobanking in the emergency department: implementation of the Mayo Clinic Emergency Department Sepsis Biorepository

2014· article· en· W2020377168 on OpenAlexfundno aff
C. Clements, JR Anderson, Johannes Uhl, MI Rudis, FR Cockerill

Bibliographic record

VenueCritical Care · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersChildren's Health FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorJapan Society for the Promotion of ScienceRussian Foundation for Basic ResearchLondon Health Sciences Centre
KeywordsBiorepositoryEmergency departmentMedicineBiobankSepsisMedical emergencyEmergency medicineInternal medicineBioinformaticsNursing

Abstract

fetched live from OpenAlex

Biomarker discovery research has not focused on the emergency department (ED) due to perceived lack of access to ED patients for study, difficult patient identification, and preemption by time-critical clinical needs. The aim of this study was to use an automated process to accrue, within the ED, a biobank of patients presenting at risk for severe sepsis.

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 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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.218
GPT teacher head0.559
Teacher spread0.342 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2014
Admission routes1
Has abstractyes

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