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Record W2808555578 · doi:10.1016/j.bja.2017.11.087

Recommendations for the nomenclature of cognitive change associated with anaesthesia and surgery—2018

2018· review· en· W2808555578 on OpenAlexfundno aff
Lisbeth Evered, Brendan Silbert, David S. Knopman, David A. Scott, Steven T. DeKosky, Lars S. Rasmussen, Esther S. Oh, G. Crosby, Miles Berger, Roderic G. Eckenhoff, David Ames, Alex Bekker, Deborah Blacker, Jeffrey N. Browndyke, Stacie Deiner, Diederik van Dijk, Maryellen F. Eckenhoff, Lars I. Eriksson, Dougas Galasko, Kirk J. Hogan, Sharon K. Inouye, Constantine G. Lyketsos, Edward R. Marcantonio, Paul Maruff, Mervyn Maze, Beverley A. Orser, Thomas H. Ottens, Catherine C. Price, Perminder S. Sachdev, Katie J. Schenning, Frederick Seiber, Jeffrey T. Silverstein, Jacob Steinmetz, Niccolò Terrando, Paula Trzapacz, Rob Whittington, Zhongcong Xie

Bibliographic record

VenueBritish Journal of Anaesthesia · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
FundersUniversity of California, San FranciscoUniversity of California, San DiegoKarolinska InstitutetCollege of Medicine, University of FloridaUniversity of TorontoInternational Anesthesia Research SocietyUniversity of New South WalesHarvard T.H. Chan School of Public HealthSchool of Medicine, Indiana UniversityRigshospitaletJohns Hopkins UniversityBristol-Myers SquibbMcKnight FoundationIcahn School of Medicine at Mount SinaiNovartisUniversity of PennsylvaniaMassachusetts General HospitalNational Institute on AgingAlzheimer's Association
KeywordsNomenclatureCognitionMedicineAnesthesiaGeneral anaesthesiaPsychologyPsychiatryBiologyTaxonomy (biology)

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)0.013
metaresearch head score (Gemma)0.039
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 score0.017
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0110.009
Science and technology studies0.0020.004
Scholarly communication0.0050.006
Open science0.0060.004
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0160.007

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 head0.091
GPT teacher head0.327
Teacher spread0.236 · 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

Citations912
Published2018
Admission routes1
Has abstractno

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