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Sedation for moderate-to-severe traumatic brain injury in adults

2025· article· en· W4410125817 on OpenAlexaff
David Williamson, Lindsay Dryden, Wei Cheng, Brian Hutton, Becky Skidmore, Sangeeta Mehta, Eyal Golan, Alexis F. Turgeon, Neill K. J. Adhikari, Louise Rose, Lisa Burry

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSunnybrook Health Science CentreThe Quebec Population Health Research NetworkHealth Sciences CentreMount Sinai HospitalOttawa HospitalUniversity Health NetworkUniversity of TorontoUniversité LavalUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsSedationTraumatic brain injuryAnesthesiaMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To assess the effects of sedative, analgesic, and anaesthetic drugs on neurological outcomes in adults with moderate-to-severe traumatic brain injury.

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)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.054
GPT teacher head0.372
Teacher spread0.318 · 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 designSystematic review
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

Citations2
Published2025
Admission routes1
Has abstractyes

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