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Record W4415806325 · doi:10.1007/s00134-025-08186-4

The intensive care medicine research agenda for the management of ICU acquired weakness: a multinational, interprofessional perspective

2025· article· en· W4415806325 on OpenAlexaff
Sabrina Eggmann, Selina M. Parry, Tessa Broadley, Gordon S. Lynch, Amy J. Bongetti, Emma J. Ridley, Elizabeth Ayre, Michael Bailey, Rinaldo Bellomo, Sue Berney, Scott Bradley, Heidi Buhr, Marion Campbell, Kelly Casey, Lee‐anne S. Chapple, Bronwen Connolly, Jai N. Darvall, Adam M. Deane, Mark A. Febbraio, Rik Gosselink, Catherine L. Granger, Kimberley Haines, Susan Hanekom, Michael O. Harhay, Meg Harrold, Kate Hayes, Greet Hermans, Alisa M. Higgins, Snigdha Jain, Michelle E. Kho, Bharath Kumar Tirupakuzhi Vijayaraghavan, Kate Lambell, Jenna Lang, I. Anne Leditschke, Itamar Levinger, Sherene Magana Cruz, Andrea P. Marshall, Dale M. Needham, George Ntoumenopoulos, Peter Nydahl, Bhakti K. Patel, Michelle Paton, Zudin Puthucheary, Olav Rooyackers, Stefan J. Schaller, Ary Serpa Neto, Christian Stoppe, Oystein Tronstad, Ilse Vanhorebeek, Werner J. Z’Graggen, Carol Hodgson

Bibliographic record

VenueIntensive Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersNational Health and Medical Research Council
KeywordsPain medicineDelphi methodMEDLINEIntensive careDosingAnesthesiologyCritically illIntensive care unitPerspective (graphical)Delphi

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)0.058
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0080.013
Scholarly communication0.0210.015
Open science0.0040.016
Research integrity0.0170.020
Insufficient payload (model declined to judge)0.0160.001

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 head0.052
GPT teacher head0.425
Teacher spread0.372 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations11
Published2025
Admission routes1
Has abstractno

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