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Record W4380998146 · doi:10.1183/23120541.00194-2023

ERS International Congress 2022: highlights from the Respiratory Clinical Care and Physiology Assembly

2023· article· en· W4380998146 on OpenAlexaff
Angelos Vontetsianos, Damla Karadeniz Güven, Sophie Betka, Sara Souto‐Miranda, Mathieu Marillier, Oliver J. Price, Chi Yan Hui, Pradeesh Sivapalan, Cristina Jácome, Andréa Aliverti, Georgios Kaltsakas, Shailesh Kolekar, Rachael A Evans, Guido Vagheggini, Cláudia Vicente, Vitalii Poberezhets, Sam Bayat, Hilary Pinnock, Frits M.E. Franssen, Ioannis Vogiatzis, M. Châabouni, Thomas Gille

Bibliographic record

VenueERJ Open Research · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsKingston General HospitalQueen's University
FundersNational Institute for Health and Care ResearchMylanUK Research and InnovationGlaxoSmithKlineAstraZeneca
KeywordsMedicineClinical PracticeRespiratory MedicineRespiratory careHealth careClinical trialAlternative medicineIntensive care medicineFamily medicinePathologySurgery

Abstract

fetched live from OpenAlex

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of the laboratory-based science, clinical trials and qualitative research that were presented during the 2022 European Respiratory Society International Congress within the sessions from the five groups of Assembly 1 (Respiratory Clinical Care and Physiology). Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, mobile/electronic health (m-health/e-health), clinical respiratory physiology, exercise and functional imaging.

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)0.046
metaresearch head score (Gemma)0.018
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: Other · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.018
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0100.006
Open science0.0030.010
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0390.023

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 head0.157
GPT teacher head0.486
Teacher spread0.329 · 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
GenreOther

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

Citations6
Published2023
Admission routes1
Has abstractyes

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