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Record W2033499896 · doi:10.1503/cjs.014813

Outcomes following surgical treatment of periprosthetic femur fractures: a single centre series

2014· article· en· W2033499896 on OpenAlexaffvenueabout
Natasha Holder, Steve Papp, Wade Gofton, Paul E. Beaulé

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPeriprostheticMedicineNonunionSurgeryComplicationFemurArthroplastyRetrospective cohort studyFemur fracture

Abstract

fetched live from OpenAlex

BACKGROUND: Periprosthetic femoral fracture after total hip arthroplasty (THA) is an increasing clinical problem and a challenging complication to treat surgically. The aim of this retrospective study was to review the treatment of periprosthetic fractures and the complication rate associated with treatment at our institution. METHODS: We reviewed the cases of patients with periprosthetic femoral fractures treated between January 2004 and June 2009. We used the Vancouver classification to assess fracture types, and we identified the surgical interventions used for these fracture types and the associated complications. RESULTS: We treated 45 patients with periprosthetic femoral fractures during the study period (15 men, 30 women, mean age 78 yr). Based on Vancouver classification, 2 patients had AL fractures, 9 had AG, 15 had B1, 24 had B2, 2 had B3 and 4 had C fractures. Overall, 82% of fractures united with a mean time to union of 15 (range 2-64) months. Fourteen patients (31%) had complications; 11 of them had a reoperation: 6 to treat an infection, 6 for nonunion and 2 for aseptic femoral component loosening. CONCLUSION: Periprosthetic fractures are difficult to manage. Careful preoperative planning and appropriate intraoperative management in the hands of experienced surgeons may increase the chances of successful treatment. However, patients should be counselled on the high risk of complications when presenting with this problem.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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

Citations35
Published2014
Admission routes3
Has abstractyes

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