Case series of vaping-associated lung illness related to use of nicotine-containing liquids
Bibliographic record
Abstract
RATIONALE There is a limited description of cases of vaping-associated lung illness (VALI) associated with users that vape nicotine-based products.OBJECTIVES The objectives of this study were to describe a series of patients that had VALI related to use of nicotine products.METHODS Investigation of reported cases of VALI in the province of Québec, using chart review, public health investigation and analysis of vaping products when available.RESULTS Six VALI cases were reported and investigated in late Summer and Fall 2019. None of the cases used tetrahydrocannabinol (THC)-based vaping products. Three were former tobacco smokers while the rest were still actively smoking. Age ranged from 34 to 82 years old. Vaping products were analyzed for 2 patients, which did not reveal any vitamin E acetate. All cases had respiratory symptoms at presentation, 2 had gastro-intestinal symptoms and 3 had constitutional symptoms. Chest computed tomography (CT)-Scan showed ground glass opacities (GGO) for all 6 cases. Five patients were hospitalized and 2 patients were admitted to the intensive care unit. No patient died. One patient had repeated bouts of hospitalizations without much improvement until VALI was diagnosed.CONCLUSIONS In Quebec, in the second half of 2019, 6 confirmed or probable VALI cases were reported and associated with the use of e-cigarettes with nicotine and flavors only (without THC), suggesting that chemicals present in the vaping liquid and/or the device itself can cause the illness. Former smokers or active smokers may also be at increased risk of developing VALI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".