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Record W4244558380 · doi:10.3410/f.727717250.793558834

Faculty Opinions recommendation of The intensive care delirium research agenda: a multinational, interprofessional perspective.

2019· dataset· en· W4244558380 on OpenAlexfundno aff
Nicola Latronico

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2019
Typedataset
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersStichting voor de Technische WetenschappenHorizon 2020 Framework ProgrammeNational Health and Medical Research CouncilPfizerEuropean CommissionMedical Research CouncilGeriatric Research Education and Clinical CenterPfizer CanadaEuropean Society of Intensive Care MedicineAstraZenecaNational Institutes of Health
KeywordsDeliriumCritically illAnesthesiologyPerspective (graphical)Multinational corporationMedicineGeriatricsIntensive carePain medicineNursingPsychologyIntensive care medicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Delirium, a prevalent organ dysfunctions in critically ill patients, is independently associated with increased morbidity.This last decade has witnessed an exponential growth in delirium research in hospitalized patients, including those critically ill, and this research has highlighted that delirium needs to be better understood mechanistically, to help foster research that will ultimately lead to its prevention and treatment.In this invited, evidence-based paper, a multinational and interprofessional group of clinicians and researchers from within the fields of critical care medicine, psychiatry, pediatrics, anesthesiology, geriatrics, surgery, neurology, nursing, pharmacy and the neurosciences, sought to address five questions:

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)0.008
metaresearch head score (Gemma)0.073
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.148
Threshold uncertainty score0.495

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1480.102

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 head0.084
GPT teacher head0.454
Teacher spread0.370 · 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
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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