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Record W2623280028 · doi:10.1186/s13054-014-0620-z

Erratum to: Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

2014· erratum· en· W2623280028 on OpenAlexafffund
Andrew Seely, Andrea Bravi, Christophe L. Herry, Geoffrey Green, André Longtin, Tim Ramsay, Dean Fergusson, Lauralyn McIntyre, Dalibor Kubelik, Donna E. Maziak, Niall D. Ferguson, Samuel M. Brown, Sangeeta Mehta, Claudio M. Martin, Gordon D. Rubenfeld, Frank J. Jacono, Gari D. Clifford, A Fazekas, John C. Marshall

Bibliographic record

VenueCritical Care · 2014
Typeerratum
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Michael's HospitalSunnybrook HospitalLondon Health Sciences CentreUniversity of TorontoCARE CanadaUniversity Health NetworkUniversity of OttawaWilfrid Laurier UniversityOttawa Hospital
FundersGroupe canadien de recherche en soins intensifs
KeywordsMedicineCritically illIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

Abstract No abstract

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)0.006
metaresearch head score (Gemma)0.089
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 score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.089
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0150.013

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 head0.301
Teacher spread0.284 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

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