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Record W4409451728 · doi:10.1016/s2213-2600(25)00054-2

Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill

2025· review· en· W4409451728 on OpenAlexafffund
Elizabeth Munroe, Alexandra B. Spicer, Andrea Castellví-Font, Ann A. Zalucky, José Dianti, Emma Graham Linck, Victor B. Talisa, Martin Urner, Derek C. Angus, Elias Baedorf-Kassis, Bryan S. Blette, Lieuwe D. J. Bos, Kevin G. Buell, Jonathan D. Casey, Carolyn S. Calfee, Lorenzo Del Sorbo, Elisa Estenssoro, Niall D. Ferguson, Rachel Giblon, Anders Granholm, Michael O. Harhay, Anna Heath, Carol S. Hodgson, Timothy T. Houle, Cong Jiang, L. Kramer, Patrick R. Lawler, Aleksandra Leligdowicz, Fan Li, Kuan Liu, Amelia W. Maiga, David M. Maslove, Colin McArthur, Daniel F McAuley, Ary Serpa Neto, Charissa Oosthuysen, Anders Perner, Hallie C. Prescott, Bram Rochwerg, Sarina K. Sahetya, Mariia Samoilenko, Mireille E. Schnitzer, Kevin P. Seitz, Faraaz Shah, Manu Shankar‐Hari, Pratik Sinha, Arthur S. Slutsky, Edward T. Qian, Steve Webb, Paul J. Young, Fernando G Zampieri, Ryan Zarychanski, Eddy Fan, Matthew W. Semler, Matthew M. Churpek, Ewan C. Goligher

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of ManitobaUniversity of AlbertaAlberta Health ServicesToronto General HospitalMcMaster UniversityQueen's UniversityUniversity of TorontoWestern UniversityUniversité de MontréalPublic Health OntarioUniversity Health NetworkMcGill University Health CentreToronto Rehabilitation InstituteFoothills Medical Centre
FundersU.S. National Library of MedicineNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNovo NordiskSwedish Orphan BiovitrumCenters for Disease Control and PreventionU.S. Department of DefenseEli Lilly and CompanyChina Scholarship CouncilZonMwCSL BehringNational Institute for Health and Care ResearchNational Institutes of HealthNovavaxWellcome TrustGenentechResearch ManitobaGlaxoSmithKlineNational Sanitarium AssociationAstraZenecaUniversity Health Network FoundationPatient-Centered Outcomes Research InstituteU.S. Department of Veterans AffairsAgency for Healthcare Research and QualityNGM BiopharmaceuticalsAmerican Thoracic Society
KeywordsMedicineCritically illIntensive care medicineCritical illnessPrecision medicineMEDLINEPathology

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)0.040
metaresearch head score (Gemma)0.063
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 score0.040
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.063
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0110.007
Bibliometrics0.0090.006
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0050.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.441
GPT teacher head0.502
Teacher spread0.062 · 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

Citations32
Published2025
Admission routes2
Has abstractno

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