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Record W3192057574 · doi:10.1016/j.bja.2021.05.038

The central nervous system during lung injury and mechanical ventilation: a narrative review

2021· review· en· W3192057574 on OpenAlexaff
Guillermo M. Albaiceta, Laurent Brochard, Claúdia C. dos Santos, Rafael Fernández, Dimitris Georgopoulos, Timothy D. Girard, Amal Jubran, Josefina López‐Aguilar, Jordi Mancebo, Paolo Pelosi, Yoanna Skrobik, Arnaud W. Thille, M. Elizabeth Wilcox, Lluís Blanch

Bibliographic record

VenueBritish Journal of Anaesthesia · 2021
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of TorontoUniversity Health NetworkMontreal Clinical Research InstituteSt. Michael's Hospital
FundersAgencia Estatal de InvestigaciónFederación Española de Enfermedades RarasEuropean Regional Development FundMinisterio de Ciencia y TecnologíaInstituto de Salud Carlos IIICentro de Investigación Biomédica en Red de Cáncer
KeywordsMedicineMechanical ventilationCentral nervous systemNarrativeLungNarrative reviewAnesthesiaIntensive care medicineLiteratureInternal medicineArt

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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
GenreReview

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

Citations56
Published2021
Admission routes1
Has abstractno

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