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Record W1983515116 · doi:10.1007/s11999-015-4224-y

Which Surgical Treatment for Open Tibial Shaft Fractures Results in the Fewest Reoperations? A Network Meta-analysis

2015· article· en· W1983515116 on OpenAlexafffund
Clary J. Foote, Gordon Guyatt, Nithin K. Vignesh, Raman Mundi, Harman Chaudhry, Diane Heels‐Ansdell, Lehana Thabane, Paul Tornetta, Mohit Bhandari

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2015
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcMaster University
FundersMedical Research CouncilMcMaster University
KeywordsMedicineOpen fractureSurgeryMeta-analysisSports medicineOrthopedic surgeryTibiaPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Open tibial shaft fractures are one of the most devastating orthopaedic injuries. Surgical treatment options include reamed or unreamed nailing, plating, Ender nails, Ilizarov fixation, and external fixation. Using a network meta-analysis allows comparison and facilitates pooling of a diverse population of randomized trials across these approaches in ways that a traditional meta-analysis does not. QUESTIONS/PURPOSES: Our aim was to perform a network meta-analysis using evidence from randomized trials on the relative effect of alternative approaches on the risk of unplanned reoperation after open fractures of the tibial diaphysis. Our secondary study endpoints included malunion, deep infection, and superficial infection. METHODS: A network meta-analysis allows for simultaneous consideration of the relative effectiveness of multiple treatment alternatives. To do this on the subject of surgical treatments for open tibial fractures, we began with systematic searches of databases (including EMBASE and MEDLINE) and performed hand searches of orthopaedic journals, bibliographies, abstracts from orthopaedic conferences, and orthopaedic textbooks, for all relevant material published between 1980 and 2013. Two authors independently screened abstracts and manuscripts and extracted the data, three evaluated the risk of bias in individual studies, and two applied Grading of Recommendation Assessment, Development and Evaluation (GRADE) criteria to bodies of evidence. We included all randomized and quasirandomized trials comparing two (or more) surgical treatment options for open tibial shaft fractures in predominantly (ie, > 80%) adult patients. We calculated pooled estimates for all direct comparisons and conducted a network meta-analysis combining direct and indirect evidence for all 15 comparisons between six stabilization strategies. Fourteen trials published between 1989 and November 2011 met our inclusion criteria; the trials comprised a total of 1279 patients surgically treated for open tibial shaft fractures. RESULTS: Moderate confidence evidence showed that unreamed nailing may reduce the likelihood of reoperation compared with external fixation (network odds ratio [OR], 0.38; 95% CI, 0.23-0.62; p < 0.05), although not necessarily compared with reamed nailing (direct OR, 0.74; 95% CI, 0.45-1.24; p = 0.25). Only low- or very low-quality evidence informed the primary outcome for other treatment comparisons, such as those involving internal plate fixation, Ilizarov external fixation, and Ender nailing. Method ranking based on reoperation data showed that unreamed nailing had the highest probability of being the best treatment, followed by reamed nailing, external fixation, and plate fixation. CIs around pooled estimates of malunion and infection risk were very wide, and therefore no conclusive results could be made based on these data. CONCLUSION: Current evidence suggests that intramedullary nailing may be superior to other fixation strategies for open tibial shaft fractures. Use of unreamed nails over reamed nails also may be advantageous in the setting of open fractures, but this remains to be confirmed. Unfortunately, these conclusions are based on trials that have had high risk of bias and poor precision. Larger and higher-quality head-to-head randomized controlled trials are required to confirm these conclusions and better inform clinical decision-making. LEVEL OF EVIDENCE: Level I, therapeutic study.

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)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.430
GPT teacher head0.544
Teacher spread0.114 · 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 designObservational
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".

Quick stats

Citations96
Published2015
Admission routes2
Has abstractyes

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