The Impact of Vaping on the Ocular Surface: A Systematic Review of the Literature
Bibliographic record
Abstract
Background: The use of electronic cigarettes has become increasingly popular in recent years. However, the impact that electronic cigarettes have on the ocular surface is not well known. Therefore, the aim of this review is to explore the current literature on the acute and chronic sequelae of electronic cigarettes on the ocular surface. Methods: A systematic review of the literature was undertaken by keyword searching on the Embase, Medline, and Web of Science databases. Articles identified through the search underwent title/abstract screening, full-text screening, and data extraction. Results: A total of 18 studies were included in this review. Non-intended ocular surface exposures and intended exposures on the ocular surface were found to be associated with the use of electronic cigarettes. Conclusions: The impact of vaping on the ocular surface is not benign. There are significant risks that vaping can pose to the ocular surface. Hence, it is necessary to develop appropriate risk communication tools given the increasing popularity of this activity. Additionally, future long-term studies are needed to better understand the long-term impacts of vaping on the ocular surface given the lack of current data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".