Case report of asthma associated with 3D printing
Bibliographic record
Abstract
BACKGROUND: Three-dimensional (3D) printing is being increasingly used in manufacturing and by small business entrepreneurs and home hobbyists. Exposure to airborne emissions during 3D printing raises the issue of whether there may be adverse health effects associated with these emissions. AIMS: We present a case of a worker who developed asthma while using 3D printers, which illustrates that respiratory problems may be associated with 3D printer emissions. CASE REPORT: The patient was a 28-year-old self-employed businessman with a past history of asthma in childhood, which had resolved completely by the age of eight. He started using 10 fused deposition modelling 3D printers with acrylonitrile-butadiene-styrene filaments in a small work area of approximately 3000 cubic feet. Ten days later, he began to experience recurrent chest tightness, shortness of breath and coughing at work. After 3 months, his work environment was modified by reducing the number of printers, changing to polylactic acid filaments and using an air purifier with an high-efficiency particulate air filter and organic cartridge. His symptoms improved gradually, although he still needed periodic treatment with a salbutamol inhaler. While still symptomatic, a methacholine challenge indicated a provocation concentration causing a 20% fall in FEV1 (PC20) of 4 mg/ml, consistent with mild asthma. Eventually, his symptoms resolved completely and a second methacholine challenge after symptom resolution was normal (PC20 > 16 mg/ml). CONCLUSIONS: This case indicates that workers may develop respiratory problems, including asthma when using 3D printers. Further investigation of the specific airborne emissions and health problems from 3D printing is warranted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".