We must join forces in the battle against COPD
Bibliographic record
Abstract
The recently published American Thoracic Society (ATS)/European Respiratory Society (ERS) statement on research questions in chronic obstructive pulmonary disease (COPD) [1] is an excellent and extensive document. It will, without doubt, contribute to better understanding of COPD and hopefully direct research to important unmet needs. Our only disappointment is that the authors did not include significant representation from primary care or public health: disappointing because globally most people with COPD are diagnosed and managed in the community by primary care clinicians, and many of the unmet individual and population research needs can only be defined and answered by professionals working in these settings. Specialists and primary care physicians must join forces in the battle against COPD ! The contributors from the International Primary Care Respiratory Group are Vidal Barchilon (Facultad de Medicina de Cádiz, Cadiz, Spain), Andrew Cave (University of Alberta, Dept of Family Medicine, Alberta, Canada), Niels Chavannes (Leiden University Medical Center, Dept of Public Health and Primary Care, Leiden, the Netherlands), Javiera Inés Corbalán Pössel (Municipal Health Department, Providencia Municipality, Santiago de Chile, Chile), Jaime Correia de Sousa (Community Health, School of Health Sciences, University of Minho, Braga, Portugal), Sofia Dimopoulou (Kassandreia Primary Care Health Center, Chalkidiki, Greece), Pedro Fonte (University of Minho, School of Health Sciences, Braga, Portugal), Antonio Infantino (Società Italiana Interdisciplinare per le Cure Primarie, Bari, Italy), Rachel Jordan (University of Birmingham, Unit of Public Health, Epidemiology and Biostatistics, Birmingham, UK), Frank Kanniess (general practitioner, Reinfeld, Germany), Oleksii Korzh (Kharkiv Medical Academy of Postgraduate Education, Dept of General Practice–Family Medicine, Kharkiv, Ukraine), Karin Lisspers (Uppsala University, Department of Public Health and Caring Sciences, Uppsala, Sweden), Le AnPham (University of Medicine and Pharmacy, Centre for Training Family Medicine, Ho Chi Minh, Viet Nam), David Price (University of Aberdeen, Primary Care, Division of Applied Health Sciences, Aberdeen, UK), Sundeep Salvi (Chest Research Foundation, Pune, India), Aziz Sheikh (The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK), Jiska Snoeck-Stroband (Leiden University Medical Center, Dept of Public Health and Primary Care, Leiden, the Netherlands), Mauricio Soto (Pontificia Universidad Católica de Chile, Department of Family Medicine, Santiago, Chile), Björn Ställberg (Uppsala University, Department of Public Health and Caring Sciences, Uppsala, Sweden), Tze LeeTan (National University of Singapore, Yong Loo Lin School of Medicine, Singapore), Mike Thomas (University of Southampton, Primary Care Research, Southampton, UK), Ioanna Tsiligianni (University of Crete, Clinic of Social and Family Medicine, Heraklion, Greece), Thys van der Molen (University Medical Center Groningen, Department of Primary Care Respiratory Medicine, Groningen, the Netherlands), Frederik van Gemert (University Medical Center Groningen, Groningen, the Netherlands), Kristine Whorlow (National Asthma Council Australia, South Melbourne, Australia), Barbara Yawn (University of Minnesota, Department of Family and Community Health, Minneapolis, MN, USA) and Osman Yusuf (The Allergy and Asthma Institute, Lahore, Pakistan).
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.025 | 0.055 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".