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Record W4318070604 · doi:10.1183/23120541.00532-2022

ERS International Congress 2022: highlights from the Respiratory Intensive Care Assembly

2023· review· en· W4318070604 on OpenAlexaff
Simon Valentin, Daniel López‐Padilla, Santi Nolasco, Darjan Ranilović, Raquel Guillamat‐Prats, Toni Marín, Sharlene Ho, Shannon Tang, Efthymia Papadopoulou, Joseph L. Malone, Sebastian Leiva Agüero, Chloé Cantero, Maxime Patout, Christoph Fisser

Bibliographic record

VenueERJ Open Research · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineARDSRespiratory careIntensive care unitIntensive care medicineIntensive careRespiratory systemRespiratory failureAcute respiratory distressSession (web analytics)COPDMechanical ventilationAcute respiratory failureLungInternal medicine

Abstract

fetched live from OpenAlex

Early Career Members of Assembly 2 (Respiratory Intensive Care) attended the 2022 European Respiratory Society (ERS) International Congress in Barcelona, Spain. The conference covered acute and chronic respiratory failure. Sessions of interest to our Assembly members and to those interested in respiratory critical care included the state-of-the-art session on respiratory critical care, the journal session (ERS/Lancet) on acute respiratory distress syndrome (ARDS) phenotyping into precision medicine, and sessions on specificity of coronavirus disease 2019 ARDS and its post-critical care. A symposium on treatment of acute respiratory failure in patients with COPD and innovations in mechanical ventilation either in the intensive care unit or at home were also reported upon. These sessions are summarised in this article.

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)0.003
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0200.009

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 head0.440
GPT teacher head0.537
Teacher spread0.097 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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