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Record W4414881520 · doi:10.1080/07853890.2025.2563000

Efficacy and safety of different fixation methods for unstable anterior pelvic ring fractures: a Bayesian network meta-analysis

2025· review· en· W4414881520 on OpenAlexaboutno aff
Dongcheng Ran, Chunqing Wang

Bibliographic record

VenueAnnals of Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsnot available
FundersGuizhou Medical UniversityNational Natural Science Foundation of ChinaHealth Commission of Guizhou Province
KeywordsFixation (population genetics)Bayesian networkRing (chemistry)PelvisBayesian probability

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effectiveness and safety of treatment methods for unstable pelvic anterior ring fractures, aiming to provide new ideas for their treatment. METHODS: We searched English databases including PubMed, Embase, Cochrane Library, and Web of Science, as well as Chinese databases CNKI, VIP, and Wanfang, with the search date up to September 10, 2024. The quality of the included studies was assessed using the Cochrane's Risk of Bias Tool and the Newcastle-Ottawa Scale (NOS). The effectiveness and safety of different surgical fixation methods were ranked according to probability levels. A total of 17 studies involving 1010 patients were included. RESULTS: Compared to external fixation (ExFix), plating, screws, and internal fixator (INFIX) showed better reduction quality. Patients using plates had the highest likelihood of achieving anatomical reduction. Screw fixation was the most effective method for postoperative functional recovery. The screw group had the lowest incidence of deep vein thrombosis in the lower limbs. The plate fixation group had the lowest incidence of lateral femoral cutaneous nerve injury. CONCLUSION: In the treatment of unstable pelvic anterior ring fractures, plating, screws, and INFIX fixation can be selected, while ExFix should only be used as a temporary fixation method.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0010.001
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 head0.150
GPT teacher head0.487
Teacher spread0.336 · 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.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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