Early Career Members at the ERS International Congress 2017: highlights from the Assemblies
Bibliographic record
Abstract
The 2017 ERS International Congress was, as always, well organised, providing participants with a good mixture of translational and clinical science. Early career members were very well represented in thematic poster, poster discussion and oral presentation sessions and were also actively involved in chairing sessions. The efforts of the Early Career Members Committee (ECMC) to increase the number of early career members included in the competence list (the list of early career members with an interest in being more actively involved in the society) paid off immensely, because the number of early career members registered improved hugely across all assemblies after the Congress. Several newly registered early career members have collated some highlights of the Congress for their assemblies, which should be of interest to all members. As assemblies 12 and 13 are new, there is no report from assembly 12 as there is not yet, at the time of writing, an early career member representative for this newly created assembly. .@EarlyCareerERS reflect on the highlights from the #ERSCongress 2017 The authors’ affiliations are as follows. Nicolas Kahn: Dept of Pneumology and Critical Care Medicine, Thorax-klinik, University Hospital Heidelberg, Heidelberg, Germany; Ioannis Tomos: 2nd Pulmonary Medicine Dept, “ATTIKON” University Hospital, National and Kapodistrian University of Athens, Athens, Greece; Vasileios Andrianopoulos: Dept of Respiratory Medicine and Pulmonary Rehabilitation, Schoen Klinik Berchtesgadener Land, Schoenau am Koenigssee, Germany; Husevin Arikan: Marmara University Hospital, Respiratory and Critical Care Medicine, Istanbul, Turkey; Anne van der Does: Dept of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands; Isaac Almendros: Unitat de Biofísica i Bioenginyeria, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain, Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Madrid, Spain and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Sara Bonvivi: Respiratory Pharmacology Group, Airway Disease Section, National Heart and Lung Institute, Imperial College London, London, UK; Ann Morgan: Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College London, London, UK; Raffaella Nenna: Dept of Pediatrics and Infantile Neuropsychiatry, “Sapienza” University of Rome, Rome, Italy; Dimitrios Magouliotis: Dept of Surgery, University Hospital of Larissa, Larissa, Greece; Matthew Rutter: Lung Function Dept, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Kevin De Soomer: Dienst Longziekten, University Hospital Antwerpen, Edegem, Belgium; Andre Nyberg: Dept of Community Medicine and Rehabilitation, Physiotherapy, Umeå University, Umeå, Sweden and Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada; Sara Lundell: Dept of Community Medicine and Rehabilitation, Physiotherapy, Umeå University, Umeå, Sweden; Katleen Leceuvre: Pneumology, University Hospital Leuven, Leuven, Belgium; Aran Singanayagam: Airways disease infection, National Heart and Lung Institute, Imperial College London, London, UK; Clementine Bostantzoglou: 7th Respiratory Medicine Dept, “Sotira” Athens Chest Hospital, Athens, Greece; Harry Karmouty-Quintana: Dept of Biochemistry and Molecular Biology, UTHealth – McGovern Medical School, Houston, TX, USA; Jana De Brandt: REVAL – Rehabilitation Research Center, BIOMED - Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.034 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".