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Record W4414081890 · doi:10.1002/jso.70083

Enhanced Functional and Surgical Outcomes With 3D Printing in Orthopedic Oncology: A Comparative Meta‐Analysis Against Conventional Techniques

2025· article· en· W4414081890 on OpenAlexaff
Peter Joseph Mounsef, Benjamin Blackman, Ojasvi Sharma, Ahmed Aoude, Anthony Bozzo

Bibliographic record

VenueJournal of Surgical Oncology · 2025
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsOrthopedic surgery3D printingOrthopedic Procedures3d printedMEDLINE

Abstract

fetched live from OpenAlex

INTRODUCTION: Three-dimensional printing (3DP) technology has increasingly gained attention in orthopedic oncology, where complex tumor resections and reconstructions demand high precision. 3DP enables the creation of patient-specific models and prostheses, which can improve postoperative quality of life for patients while assisting surgeons in preoperative planning, enhancing surgical accuracy, and improving outcomes in complex oncologic cases. Despite its potential, comprehensive data on the effectiveness and applications of 3DP in orthopedic oncology are limited. This paper assesses whether using 3DP compared to conventional techniques results in better outcomes in orthopedic oncology. METHODS: A comprehensive search of Ovid MEDLINE, Embase, Scopus, and Web of Science was conducted until November 2024. Studies comparing 3D printing to conventional methods in orthopedic oncology and reporting outcomes such as operative time, blood loss, recurrence rates, or functional scores were included. Weighted means and meta-analyses were conducted to compare these outcomes. Statistical heterogeneity was adjusted by using a random-effects model. RESULTS: Fourteen studies comprising 478 patients met the inclusion criteria. Our primary findings were improved MSTS scores (mean difference [MD]: 2.17, p = 0.00) and decreased blood loss (MD: -69.8 mL, p = 0.00) in the 3D printing groups. There was no significant difference in operative time between 3D printing and conventional techniques (MD: -12.2 min, p = 0.32). Tumor recurrence rates did not differ significantly between groups (relative risk: 0.88, p = 0.50). Subgroup analyses indicated that 3D-printed implants showed the greatest benefit in reducing OR time and blood loss, with the other subgroups showing no significant difference in OR time, blood loss, or recurrence rate. CONCLUSION: The findings suggest that 3D printing in orthopedic oncology may enhance surgical precision by reducing OR time, intraoperative blood loss, and improving postoperative function, without affecting recurrence rates. Substantial heterogeneity limits confidence in these findings. LEVEL OF EVIDENCE: Level III.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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