MétaCan
Menu
Back to cohort
Record W3200099356 · doi:10.7759/cureus.18113

The Designing, Testing, and Utility of a 3D-Printed Respirator: A Hospital's Journey Into Self-Sustainability During COVID-19

2021· article· en· W3200099356 on OpenAlexaff
Aditya Lal Vallath, Ravisha More, Satyajeet Bhaskare, Sarabjeet Rattan, Ajinkya Athlye, Adheeth Praveen, Bindi S Patel, Vyom Richharia, Akshita Lalendran, Sudhir Patsute

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsGlycemic Index Laboratories
Fundersnot available
KeywordsRespiratorPersonal protective equipmentCoronavirus disease 2019 (COVID-19)Health careEconomic shortageBusinessPandemicOperations managementMedical emergencyMedicineEngineeringInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Objective The current global COVID-19 pandemic has disrupted supply chains and the production of essential goods and services. This includes personal protective equipment (PPE) kits, respirators, and other protective devices. Hence efforts were made to prototype and produce 3D-printed N95 respirators to fill the gap in supply. In addition, methods of sterilization were put into place for the respirators. As well as forming standard operating procedures. Methods With the use of vast open-source libraries and collaboration with engineers and doctors fighting the COVID-19 pandemic, respirator prototypes were produced with special consideration to the sizing to fit median facial sizes. Polymer plastics were mixed in various proportions to condition the respirator to be used by frontline workers in austere environments. Due to the shortage of medical-grade filter media, alternative sources were researched. Merv 13 and Merv 15 filters were selected due to their cheap costs, vast abundance, and proven filtration efficacy against particles of 0.03 microns. Studies conducted around the world have also shown its efficacy as an alternative to medical-grade air filter media. After developing standard operating procedures (SOPs) for sterilisation and respirator usage. Emergency approval was obtained and a limited number of healthcare workers were issued with this respirator (n=400). PPE kit satisfaction and self-efficacy scores were calculated from daily questionnaires during donning and doffing Results Qualitative fit-tests in all 400 healthcare workers matched those of a conventional N95 respirator. Almost all of the respondents in the PPE kit satisfaction responded positively. The self-efficacy score calculated from the general self-efficiency scale had an overall positive value, with the average score being 4.29. This demonstrated that the self-efficacy score was above average and indicated a high motivation to overcome obstacles and spend more time solving problems. The average self-efficacy score is defined between 2.5 - 3.5, and a low self-efficacy score is defined as a score below 2.5. Lastly, a regression analysis was done to test the correlation between PPE kit satisfaction and self-efficiency this demonstrated a positive correlation between PPE kit satisfaction using the 3D-printed respirator and self-efficacy (Slope: 0.416, Intercept: -1.066, R-value: 0.872, P-value: <0.01) Conclusions With supply chain disruptions and reduced or nonexistent supplies of essential medical goods. The need of a reusable, sterilisable, and efficient respirator has never been more evident. The materials used have made it sustain heavy use in austere environments. Studies have reported higher than average burnout rates in COVID-19-based healthcare workers. Studies have also shown that the rates of burnout are high in healthcare professionals without access to proper PPE kits in developing nations. This respirator was rated highly in PPE kit satisfaction and the self-efficacy score. Studies have demonstrated a correlation between high self-efficacy scores and low burnout rates in health care workers. There is also documented evidence of a positive correlation between high self-efficacy scores and general health. As the pandemic continues to evolve, so will the efforts to combat it, such as 3D printing. Interdisciplinary collaboration continues to drive our efforts to combat the pandemic and hopefully resolve it in the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.025
GPT teacher head0.313
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicInfection Control and VentilationFrench-language works237,207