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Record W4401715645 · doi:10.1007/s00134-024-07578-2

Tracheal intubation in critically ill adults with a physiologically difficult airway. An international Delphi study

2024· article· en· W4401715645 on OpenAlexaff
Kunal Karamchandani, Prashant Nasa, Mary Jarzebowski, David Brewster, Audrey De Jong, Philippe R. Bauer, Lauren Berkow, Calvin A. Brown, Luca Cabrini, Jonathan D. Casey, Tim Cook, Jigeeshu Vasishtha Divatia, Laura V. Duggan, Louise Ellard, Begüm Ergan, Malin Jonsson Fagerlund, Jonathan Gatward, Robert Greif, A. Higgs, Samir Jaber, D Janz, Aaron M. Joffe, Boris Jung, George Kovács, Arthur Kwizera, John G. Laffey, Jean-Baptiste Lascarrou, J. Adam Law, Stuart Marshall, Brendan McGrath, Jarrod Mosier, Daniel Perin, Oriol Roca, Amélie Rollé, Vincenzo Russotto, John C. Sakles, Gentle Sunder Shrestha, Nathan J. Smischney, Massimiliano Sorbello, Avery Tung, Craig S. Jabaley, Sheila Nainan Myatra, Kariem El‐Boghdadly, Anna M. Budde, Stephen R. Estimé, Kristina Goff, Rachel Kadar, Ashish Khanna, Crystal Manohar, Gerald Matchett, Ronald G. Pearl, Robert D. Stevens, Habib Srour

Bibliographic record

VenueIntensive Care Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsDalhousie UniversityUniversity of Ottawa
FundersFeinberg School of MedicineHebrew University of JerusalemSociety of Critical Care AnesthesiologistsWake Forest School of MedicineJohns Hopkins UniversityUniversity of Chicago MedicineUniversity of MinnesotaSchool of Medicine, Stanford UniversityNorthwestern University
KeywordsMedicineDelphi methodIntubationIntensive care medicineAirway managementAirwayChecklistCritically illIntensive careTracheal intubationAnesthesiologyMedical emergencyAnesthesiaPsychology

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)0.007
metaresearch head score (Gemma)0.016
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.328
Teacher spread0.307 · 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

Citations61
Published2024
Admission routes1
Has abstractno

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