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Record W4386102775 · doi:10.1016/s2213-2600(23)00237-0

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

2023· article· en· W4386102775 on OpenAlexaff
Pratik Sinha, V. Eric Kerchberger, Andrew Willmore, Julia Chambers, Hanjing Zhuo, Jason Abbott, Chayse Jones, Nancy Wickersham, Nelson Wu, Lucile Neyton, Charles Langelier, Eran Mick, June He, Alejandra Jáuregui, Matthew M. Churpek, A.D. Gomez, Carolyn M. Hendrickson, Kirsten N. Kangelaris, Aartik Sarma, Aleksandra Leligdowicz, Kevin Delucchi, Kathleen D. Liu, James A. Russell, Michael A. Matthay, Keith R. Walley, Lorraine B. Ware, Carolyn S. Calfee

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaWestern University
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNational Institutes of HealthChina Scholarship CouncilEli Lilly and Company
KeywordsARDSMedicineSepsisAcute respiratory distressObservational studyPhenotypeRandomized controlled trialIntensive care medicineCritically illSevere sepsisRespiratory distressInternal medicineSeptic shockLungSurgeryGeneGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.031
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.049
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0080.014
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.223
GPT teacher head0.445
Teacher spread0.222 · 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 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

Citations194
Published2023
Admission routes1
Has abstractno

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