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Fracture-related infection after internal fixation of pelvic and acetabular fractures

2025· article· en· W4412844185 on OpenAlexaff
Daniel Axelrod, Andrew L. Foster, Jacelle Warren, Andrej Trampuž, Kevin Tetsworth, Michael Schuetz

Bibliographic record

VenueThe Bone & Joint Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineLogistic regressionInternal fixationRetrospective cohort studyComplicationPopulationCohortSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Aims: Fracture-related infection (FRI) is a relatively common and severe complication following operatively managed fractures. Patients with pelvic and acetabular fractures (PAFs) are a high-risk patient group, represent high injury severity, and are often polytraumatized. The aims of this study are to characterize a population-based cohort of patients with PAF complicated by FRI, to identify factors associated with the development of FRI and estimate the health economic burden. Methods: A retrospective analysis of a ten-year population-based, person-linked dataset was performed, including all PAF patients undergoing internal fixation within Queensland, Australia. Demographic and clinical variables were collected and included into a multivariable logistic regression. Health economic variables were compared between infected and uninfected PAF patients. Results: There were 842 patients who sustained a PAF and underwent operative stabilization, of which 52 (6.4%) were complicated by FRI. Open fractures, increased length of stay, and rural residence were associated with an increased risk of development of FRI. Direct inpatient costs for managing FRI were higher at a median $AUD 69,088 (IQR $44,338 to $104,297) versus $AUD 40,904 (IQR $27,803 to $59,371) for uninfected PAF management. Conclusion: The health economic impact is significant, with infected patients staying 2.5 times longer in hospital and costing 1.7 times more in direct inpatient costs compared to uninfected patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.265
Teacher spread0.259 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Bone & Joint JournalSame topicPelvic and Acetabular InjuriesFrench-language works237,207