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Record W2193609478 · doi:10.1186/2197-425x-3-s1-a23

Outcome of Manuscripts Rejected From Intensive Care Medicine: An In Silico Study

2015· article· en· W2193609478 on OpenAlexaff
Giuseppe Citerio, Chiara Marzorati, JF Timsit, Anders Perner, Jan Bakker, Matteo Bassetti, Dominique Benoît, JR Curtis, GS Doig, Margaret S. Herridge, Samir Jaber, Laurent Papazian, Michael Jacob Peters, Pierre Singer, Martin Smith, Márcio Soares, Antoni Torres, Antoine Vieillard‐Baron, Élie Azoulay

Bibliographic record

VenueIntensive Care Medicine Experimental · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNarrative reviewIntensive careAlternative medicineOutcome (game theory)Intensive care medicinePathology

Abstract

fetched live from OpenAlex

In 2013, Intensive Care medicine received 1516 manuscripts, with a global acceptance rate of 20.7%. Among the full length articles (original articles, narrative review, systematic review/meta-analyses, my paper 20y later), 1093 were original articles and 10.07% were accepted.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.304
GPT teacher head0.553
Teacher spread0.249 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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