Use of 3D printed connectors to redesign full face snorkeling masks in the COVID-19 era: A preliminary technical case-study
Bibliographic record
Abstract
The COVID-19 pandemic resulted in severe shortages of personal protection equipment and non-invasive ventilation devices. As traditional supply chains could not meet up with the demand, makeshift solutions were developed and locally manufactured by rapid prototyping networks. Among the different global initiatives, retrofitting of full-face snorkeling masks for Non-Invasive-Ventilation (NIV) applications seems the most challenging. This article provides a systematic overview of rapid prototyped - 3D printed - designs that enable attachment of medical equipment to snorkeling masks, highlighting potential and challenges in additive manufacturing. The different NIV connector designs are compared on low-cost 3D fabrication time and costs, which allows a rapid assessment of developed connectors for health care workers in urgent need of retrofitting snorkeling masks for NIV purposes. Challenges and safety issues of the rapid prototyping approach for healthcare applications during the pandemic are discussed as well. When critical parameters such as the final product cost, geographical availability of the feedstock and the 3D printers and the medical efficiency of the rapid prototyped products are well considered before deploying decentralized 3D printing as manufacturing method, this rapid prototyping strategy contributed to reduce personal protective equipment and NIV shortages during the first wave of the COVID-19 pandemic. It is also concluded that it is crucial to carefully optimize material and printer parameter settings to realize best fitting and airtight connector-mask connections, which is heavily depending on the chosen feedstock and type of printer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".