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Record W3168151395 · doi:10.37051/mir-00055

Reports from the 1st Young Investigator’s Day of the French Intensive Care Society

2021· article· en· W3168151395 on OpenAlexaff
CRT De la SRLF, Tamara Merz, Benjamine Sarton, Youenn Jouan, François Bagate, Inès Bendib Le Lan, Benoît Brassart, Nicole Denoix, Alexandre Elabbadi, Fabrice Ferré, Maud Loiselle, Gabriel Masson, Guillaume Millot, Elodie Salvador, Britta Trautwein, Fabrice Uhel, Peter Radermacher, Guillaume Voiriot, Mehdi Oualha, Éric Azabou, Boris Jung, Stein Silva, Sébastien Préau, Nicolas de Prost, Lara Zafrani, Dominique Vodovar

Bibliographic record

VenueMédecine Intensive Réanimation · 2021
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsIntensive careDiversity (politics)Translational researchWork (physics)Medical educationPsychologyMedicineLibrary scienceSociologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Translational Research Committee of the French Intensive Care Society organized the first Young Investigator’s Day on October 18th 2019. This seminar gave young Intensive Care students the opportunity to present their Master’s or PhD research work to a college of expert researchers. For this first event, Professors Jean-Marc Cavaillon (Paris), Laurent Papazian (Marseille), Peter Radermacher (Ulm) et Hafid Ait-Oufella (Paris) kindly accepted to give young candidates their critical support. The subjects of presentations, covering the fields of neuroscience, immunology, hemodynamics and pharmacology illustrated the richness and diversity of translational research in Intensive Care Medicine.

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)0.013
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0250.007

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueMédecine Intensive RéanimationSame topicIntensive Care Unit Cognitive DisordersFrench-language works237,207