MétaCan
Menu
Back to cohort
Record W4404976302 · doi:10.21037/ajo-24-25

Air quality assessment of dissecting a J750 three-dimensional printed temporal bone and recommendations 2024

2024· article· en· W4404976302 on OpenAlexaff
Emma Kanaganayagam, Joan Bowman, David P. Forrestal, Roozbeh Fakhr, Jean-Michel Bourque, Michael Wagels

Bibliographic record

VenueAustralian Journal of Otolaryngology · 2024
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsQuality (philosophy)3d printedComputer scienceMedicineBiomedical engineeringPhysics

Abstract

fetched live from OpenAlex

Background: Three-dimensional (3D) printing is a method that is used to create models for the use of simulated temporal bone dissection and has increasing availability in biomedical departments. The advantages of 3D printing are cheaper cost, transportability, readily accessible dissection with minimal preparation and reduced ethical restrictions in comparison to the use of cadaveric specimens. A high standard of likeness to cadaveric anatomy is seen with the use of a multi-material jet printer which can account for the variation in qualities of the hard bone and soft tissue structures within the middle ear and temporal bone. There remains a concern that the heat of drilling the materials may create harmful exposure of inhalable fumes and aerosols including volatile organic compounds (VOCs). The purpose of this study was to evaluate the air quality proximal to the dissector of 3D printed materials which are currently commonly used for printing temporal bones and provide a recommendation for dissecting these materials. Methods: An occupational safety assessment was performed in a simulated temporal bone laboratory with four different printed materials cited in the literature (acrylonitrile butadiene styrene, polylactic acid, white resin, multi-material). These were used to print temporal bone models with three different 3D printing processes (fused filament fabrication, multi-material jetting, stereolithography). An otolaryngology/head and neck surgical registrar conducted a temporal bone dissection on the models while wearing air quality sampling badges to determine the aerosol exposure by measuring the VOCs and total inhalable particles and respirable particles in the air proximal to the dissector. Results: The results of the samples from the badges revealed that the individual VOC samples and particulate counts (respirable and inhalable particles) were well below the maximum limit recommended concentrations [Safe Work Australia (SWA) and Australian Institute of Occupational Hygienists (AIOH)]. Conclusions: The study findings reveal that dissection of 3D printed temporal bones, in particular the multi-material Stratasys J750 printed models, can be dissected within the safe limits of AIOH and SWA standards to inhalable, respirable particles and individually tested VOCs. However, our recommendations include the use of personal protective equipment, local extraction, and ventilation within the room.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.060
GPT teacher head0.379
Teacher spread0.320 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAustralian Journal of OtolaryngologySame topicNasal Surgery and Airway StudiesFrench-language works237,207