MétaCan
Menu
Back to cohort

Periprosthetic joint infection following hip hemiarthroplasty

2022· article· en· W4310497373 on OpenAlexaff
Jonathan Bourget-Murray, Isabel Horton, Jared Morris, Antoine Bureau, Simon Garceau, Hesham Abdelbary, George Grammatopoulos

Bibliographic record

VenueBone & Joint Open · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsPeriprostheticJoint (building)MedicineJoint infectionsSurgeryArthroplastyEngineeringStructural engineering

Abstract

fetched live from OpenAlex

AIMS: The aims of this study were to determine the incidence and factors for developing periprosthetic joint infection (PJI) following hemiarthroplasty (HA) for hip fracture, and to evaluate treatment outcome and identify factors associated with treatment outcome. METHODS: A retrospective review was performed of consecutive patients treated for HA PJI at a tertiary referral centre with a mean 4.5 years' follow-up (1.6 weeks to 12.9 years). Surgeries performed included debridement, antibiotics, and implant retention (DAIR) and single-stage revision. The effect of different factors on developing infection and treatment outcome was determined. RESULTS: A total of 1,984 HAs were performed during the study period, and 44 sustained a PJI (2.2%). Multiple logistic regression analysis revealed that a higher CCI score (odds ratio (OR) 1.56 (95% confidence interval (CI) 1.117 to 2.187); p = 0.003), peripheral vascular disease (OR 11.34 (95% CI 1.897 to 67.810); p = 0.008), cerebrovascular disease (OR 65.32 (95% CI 22.783 to 187.278); p < 0.001), diabetes (OR 4.82 (95% CI 1.903 to 12.218); p < 0.001), moderate-to-severe renal disease (OR 5.84 (95% CI 1.116 to 30.589); p = 0.037), cancer without metastasis (OR 6.42 (95% CI 1.643 to 25.006); p = 0.007), and metastatic solid tumour (OR 15.64 (95% CI 1.499 to 163.087); p = 0.022) were associated with increasing PJI risk. Upon final follow-up, 17 patients (38.6%) failed initial treatment and required further surgery for HA PJI. One-year mortality was 22.7%. Factors associated with treatment outcome included lower preoperative Hgb level (97.9 g/l (SD 11.4) vs 107.0 g/l (SD 16.1); p = 0.009), elevated CRP level (99.1 mg/l (SD 63.4) vs 56.6 mg/l (SD 47.1); p = 0.030), and type of surgery. There was lower chance of success with DAIR (42.3%) compared to revision HA (66.7%) or revision with conversion to total hip arthroplasty (100%). Early-onset PJI (≤ six weeks) was associated with a higher likelihood of treatment failure (OR 3.5 (95% CI 1.2 to 10.6); p = 0.007) along with patients treated by a non-arthroplasty surgeon (OR 2.5 (95% CI 1.2 to 5.3); p = 0.014). CONCLUSION: 2022;3(12):924-932.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBone & Joint OpenSame topicOrthopedic Infections and TreatmentsFrench-language works237,207