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Record W4377245534 · doi:10.1056/evidoa2300034

Patient-Level Meta-Analysis of Low-Dose Hydrocortisone in Adults with Septic Shock

2023· article· en· W4377245534 on OpenAlexaff
Romain Pirracchio, Djillali Annane, Andre Kurepa Waschka, François Lamontagne, Yaseen M. Arabi, Pierre‐Édouard Bollaert, Laurent Billot, Bin Du, Josef Briegel, Jeremy Cohen, Simon Finfer, Anthony Gordon, Naomi Hammond, Hervé Hyvernat, Didier Keh, Yi Li, Ling Liu, G. Umberto Meduri, Liliana Mirea, John Myburgh, Charles L. Sprung, Neijla Tilouche, Surat Tongyoo, Balasubramanian Venkatesh, Ruiqiang Zheng, Anthony Delaney

Bibliographic record

VenueNEJM Evidence · 2023
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
FundersNIHR Imperial Biomedical Research CentreNational Health and Medical Research CouncilMedical Research CouncilAgence Nationale de la Recherche
KeywordsMedicineFludrocortisoneSeptic shockConfidence intervalHydrocortisoneRelative riskIntensive care unitInternal medicineMeta-analysisPlaceboAnesthesiaAdverse effectSepsis

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.022
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.048
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.248
GPT teacher head0.375
Teacher spread0.128 · 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 designMeta-analysis
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

Citations64
Published2023
Admission routes1
Has abstractyes

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