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Record W4307309770 · doi:10.31399/asm.hb.v23a.a0006902

Developments and Trends in Additively Manufactured Medical Devices

2022· book-chapter· en· W4307309770 on OpenAlex
Shervin Foroughi, Mahdi Derayatifar, Mohsen Habibi, Muthukumaran Packirisamy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueASM International eBooks · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsConcordia University
Fundersnot available
KeywordsFood and drug administrationMedicineMedical deviceSpecialtyMedical physicsEngineeringMedical emergencyFamily medicineBiomedical engineering

Abstract

fetched live from OpenAlex

Abstract Additive manufacturing (AM), or three-dimensional (3D) printing, is a class of manufacturing processes that create the desired geometries of an object, or an assembly of objects, layer by layer or volumetrically. AM has been used extensively for manufacturing medical devices, due to its versatility to satisfy the specific needs of an intended medical field for the product/device. This article provides a comprehensive review of AM in medical devices by the medical specialty panels of the Food and Drug Administration (FDA) Code of Federal Regulations, Parts 862 to 892, including anesthesiology, ear and nose, general hospital, ophthalmic, plastic surgery, radiology, cardiovascular, orthopedic, dental, neurology, gynecology, obstetrics, physical medicine, urology, toxicology, and pathology. It is classified under these panels, and critical reviews and future outlooks are provided. The application of AM to fabricate medical devices in each panel is reviewed; lastly, a comparison is provided to reveal relevant gaps in each medical field.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.989
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.218 · 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