MétaCan
Menu
Back to cohort
Record W4362521337 · doi:10.1007/s00134-023-07033-8

ERS/ESICM/ESCMID/ALAT guidelines for the management of severe community-acquired pneumonia

2023· article· en· W4362521337 on OpenAlexafffund
Ignacio Martín‐Loeches, Antoni Torres, Blin Nagavci, Stefano Aliberti, Massimo Antonelli, Matteo Bassetti, Lieuwe D. J. Bos, James D. Chalmers, Lennie Derde, Jan J. De Waele, José Garnacho‐Montero, Marin H. Kollef, Carlos M. Luna, Rosario Menéndez, Michael S. Niederman, Dmitry Ponomarev, Marcos I. Restrepo, David Rigau, Marcus J. Schultz, Emmanuel Weiss, Tobias Welte, Richard G. Wunderink

Bibliographic record

VenueIntensive Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Toronto
FundersEuropean Society of Intensive Care MedicineShionogiZonMwPfizerEuropean Respiratory SocietyMcMaster UniversityEuropean Society of Clinical Microbiology and Infectious DiseasesNabriva Therapeutics
KeywordsMedicineCommunity-acquired pneumoniaAnesthesiologyPain medicinePneumoniaIntensive care medicineInternal medicinePathology

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)0.006
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0060.005

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 head0.123
GPT teacher head0.391
Teacher spread0.268 · 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
GenreMethods

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

Citations285
Published2023
Admission routes2
Has abstractno

Explore more

Same venueIntensive Care MedicineSame topicPneumonia and Respiratory InfectionsFrench-language works237,207