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Record W3039666772 · doi:10.1302/2058-5241.5.190024

Three-dimensional printing in orthopaedic surgery: a scoping review

2020· review· en· W3039666772 on OpenAlex

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

VenueEFORT Open Reviews · 2020
Typereview
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOrthopedic surgeryThree dimensional printingSurgical proceduresSurgeryImplantMedical physicsGeneral surgery3D printingEngineering

Abstract

fetched live from OpenAlex

Abstract Three-dimensional printing (3DP) has become more frequently used in surgical specialties in recent years. These uses include pre-operative planning, patient-specific instrumentation (PSI), and patient-specific implant production. The purpose of this review was to understand the current uses of 3DP in orthopaedic surgery, the geographical and temporal trends of its use, and its impact on peri-operative outcomes One-hundred and eight studies (N = 2328) were included, published between 2012 and 2018, with over half based in China. The most commonly used material was titanium. Three-dimensional printing was most commonly reported in trauma (N = 41) and oncology (N = 22). Pre-operative planning was the most common use of 3DP (N = 63), followed by final implants (N = 32) and PSI (N = 22). Take-home message: Overall, 3DP is becoming more common in orthopaedic surgery, with wide range of uses, particularly in complex cases. 3DP may also confer some important peri-operative benefits. Cite this article: EFORT Open Rev 2020;5:430-441. DOI: 10.1302/2058-5241.5.190024

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.101
GPT teacher head0.369
Teacher spread0.268 · 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