The carbon footprint of respiratory treatments in Europe and Canada: an observational study from the CARBON programme
Bibliographic record
Abstract
Climate change represents a global challenge and nations are increasingly looking to decarbonise their economies by developing roadmaps for reducing greenhouse gas (GHG) emissions in accordance with international treaties, such as the Paris Agreement [1]. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Christer Janson reports personal fees from AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline PLC, Novartis and Teva outside the submitted work. Conflict of interest: Alexander Wilkinson is a member of the Montreal protocol Medical and Chemical Technical Options Committee and has made unpaid contributions to publications on the carbon footprint of inhalers and respiratory treatment which were sponsored by GlaxoSmithKline and AstraZeneca. Conflict of interest: Erika Penz has received honoraria and consulting fees from AstraZeneca, GlaxoSmithKline, Sanofi Genzyme, International Centre for Evidence-Based Medicine in Canada and Boehringer Ingelheim. Conflict of interest: Alberto Papi reports grants and personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Chiesi Farmaceutici, Menarini and Sanofi/Regeneron; personal fees from Mundipharma, Zambon, Novartis, Edmond Pharma and Roche; and grants from Fondazione Maugeri and Fondazione Chiesi. Conflict of interest: Claus F. Vogelmeier has delivered presentations at symposia and/or served on scientific advisory boards sponsored by Aerogen, AstraZeneca, Boehringer Ingelheim, CSL Behring, Chiesi, GlaxoSmithKline, Grifols, Menarini, Novartis, Nuvaira and MedUpdate. Conflict of interest: Maciej Kupczyk reports grants from AstraZeneca and personal fees from AstraZeneca, Chiesi, GlaxoSmithKline, Novartis, Lekam, Alvogen, Emma, Nexter and Berlin Chemie. Conflict of interest: Ekaterina Maslova, Nigel Budgen and John Bell are employees of AstraZeneca. Conflict of interest: Andrew Menzies-Gow has attended advisory boards for GlaxoSmithKline, Novartis, AstraZeneca, Sanofi and Teva. He has received speaker fees from Novartis, AstraZeneca, Vectura, Teva and Roche. He has also participated in research with AstraZeneca and attended international conferences with Teva. He has consultancy agreements with AstraZeneca, Vectura and Sanofi.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".