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Record W3134339281 · doi:10.1016/j.cjtee.2021.02.007

Three-dimensional printing combined with open reduction and internal fixation versus open reduction and internal fixation in the treatment of acetabular fractures: A systematic review and meta-analysis

2021· review· en· W3134339281 on OpenAlexaboutno aff
Dongpeng Tu, Yikang Yu, Zhen Liu, Wenkai Zhang, Xin Fan, Chao Xu

Bibliographic record

VenueChinese Journal of Traumatology · 2021
Typereview
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsInternal fixationMedicineCochrane LibraryMeta-analysisRandomized controlled trialSurgeryReduction (mathematics)Internal medicine

Abstract

fetched live from OpenAlex

PURPOSE: This meta-analysis compared the clinical outcome of three-dimensional (3D) printing combined with open reduction and internal fixation (ORIF) to traditional ORIF in the treatment of acetabular fractures. METHODS: We searched the Cochrane Library, PubMed, Embase, VIP database, CNKI, and Wanfang database with keywords "acetabular fracture", "3D printing", "three-dimensional printing", "open reduction and internal fixation", "Acetabulum", "Acetabula" from January 2000 to March 2020. Two reviewers independently selected articles, extracted data, assessed the quality evidence and risk bias of included trials using the Cochrane Collaboration' s tools and/or Newcastle-Ottawa scale. When the two analysts had different opinions, they would ask the third analyst for opinion. Randomized controlled trials or retrospective comparative studies of 3D printing combined with ORIF (3D printing group) versus traditional ORIF (conventional group) in the treatment of acetabular fractures were selected. The data of operation time, intraoperative blood loss, intraoperative fluoroscopy times, incidence of complications, excellent and good rate of Matta score for reduction, and excellent and good rate of hip function score were extracted. Stata14.0 statistical software was used for data analysis. RESULTS: Altogether 9 articles were selected, including 5 randomized controlled trials and 4 retrospective studies. A total of 467 patients were analyzed, 250 in the conventional group, and 217 in the 3D printing group. The operation time in the 3D printing group was less than that in the conventional group and the difference was statistically significant (standardized mean difference (SMD) = -1.19, 95% CI: -1.55 to -0.82, p < 0.05). The intraoperative bleeding volume of the 3D printing group was significantly lower than that of the conventional group (SMD = -1.08, 95% CI: -1.65 to -0.51, p < 0.05). The fluoroscopy times were less in the 3D printing group than in the conventional group and the difference was statistically significant (SMD = -1.64, 95% CI: -2.35 to -0.93, p < 0.05). The total incidence of complications in the 3D printing group was significantly lower than that in the conventional group (OR = 0.43, 95% CI: 0.24-0.79, p < 0.05). There was no significant difference in the excellent and good rate of Matta score for reduction between the two groups (OR = 0.60, 95% CI: 0.34-1.06, p > 0.05). There was no significant difference in the excellent and good rate of hip function score at the end of postoperative follow-up between the two groups (OR = 0.84, 95% CI: 0.46-1.56, p > 0.05), but the follow-up time varies from 6 months to 40 months. CONCLUSION: Compared with traditional ORIF, 3D printing combined with ORIF has certain advantages in terms that 3D printing not only helps surgeons to understand acetabular fractures more intuitively, but also effectively reduces operation time, intraoperative blood loss, intraoperative fluoroscopy times, and postoperative complications. However, there were no significant differences in the excellent and good rate of Matta score for reduction and the excellent and good rate of hip function score at the end of follow-up.

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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.446
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.078
GPT teacher head0.397
Teacher spread0.319 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations10
Published2021
Admission routes1
Has abstractyes

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