E-cigarettes: What evidence links vaping to acute lung injury and respiratory failure?
Bibliographic record
Abstract
On September 18, 2019, the Middlesex-London Health-Unit (London, Canada) reported that a previously healthy, high-school student who had vaped nicotine daily, required hospitalization for life-support and had recovered. A week later, another single case was reported in Montreal, Quebec. These Canadian cases followed published cases of e-cigarette lung toxicity including a recent cluster of 53 patients in Illinois and Wisconsin and a 5 patient-cluster in North Carolina, both published on September 6, 2019. As of October 2019, there have been 26 deaths and 1,299 cases of lung injury linked to e-cigarettes. These reports have created widespread concern among clinicians and the public, creating a need to understand what we know at this point, with the caveat being that there is no clear understanding of the causes of vaping-related lung-toxicity. Most patients were previously healthy teenagers or young-adults with progressive dyspnea, hypoxemia, nausea and tachypnea with no evidence of bacterial infection while reporting recent e-cigarette use with nicotine and/or tetrahydrocannabinol. Chest computed-tomography findings included hypersensitivity-pneumonitis, diffuse alveolar hemorrhage, consolidation and ground-glass opacities. Most patients were treated with corticosteroids and symptoms resolved so that hospital discharge and community-based care could be undertaken. There have been no reports of long-term follow-up in survivors.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".