Lung transcriptome of e-cigarette users reveals changes related to chronic lung disease
Bibliographic record
Abstract
Approximately 1-in-10 adolescents use e-cigarette devices, with 25% reporting daily use [1]. Most youth who vape have never used traditional cigarettes, creating an emerging epidemic of nicotine addiction fueled by use of e-cigarettes [2]. Public perception is that e-cigarettes are less harmful than traditional cigarettes, a belief that may be fueled, in part, by a handful of tobacco industry-funded studies reporting null results in cells or animals, replicating past misinformation campaigns about traditional cigarettes [3]. 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: Pierre-Régis Burgel reports support for the present manuscript from Association Vaincre la Mucoviscidose, Société Française de la Mucoviscidose, Filière Maladie Rare Muco CFTR. In addition, Pierre-Régis Burgel reports grants from Vertex Pharmaceuticals, GSK; consulting fees from Astra Zeneca, Chiesi, GSK, Insmed, Vertex, Viatris, Zambon; travel support Astra Zeneca, Chiesi; outside the submitted work. Conflict of interest: No conflicts to report. Research funded by a grant from the Canadian Institute for Health Research (CIHR HEV-172883).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".