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Record W3106406630 · doi:10.7759/cureus.11390

Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty in Patients With Sickle Cell Disease at King Fahad Hospital Hofuf: A Cross-Sectional Study

2020· article· en· W3106406630 on OpenAlexaboutno aff
Mohammad Alsaleem, Hassan A Alalwan, Abdullah M Alkhars, Abdullah H Al Huwaiyshil, Wejdan M Alamri

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriprostheticAvascular necrosisSurgeryArthroplastyFemoral head

Abstract

fetched live from OpenAlex

Background Patients with avascular necrosis related to sickle cell disease (SCD) can be severely disabled by the severe degenerative changes of their hip. Total hip arthroplasty (THA) remains the only surgical option for some of these patients. Total hip arthroplasty can be a challenging procedure, and SCD patients demonstrate high percentages of medical, intraoperative, and postoperative complications and implant failure. Furthermore, the need for THA following avascular necrosis in the Eastern Province of Saudi Arabia is high, and the subsequent risk of periprosthetic fracture is prevalent. Therefore, it is crucial to conduct such a study. Aim of the study This cross-sectional retrospective study aimed to assess the prevalence and associated risk factors for periprosthetic fractures during total hip arthroplasty in sickle cell disease patients at King Fahad Hospital Hofuf, Saudi Arabia. Methods We collected the data of all SCD patients who had undergone THA during the study period, January 2015 to September 2020. Forty-nine SCD patients who had undergone THA during the study period were included. Patients who had undergone hip hemiarthroplasty, postoperative fractures, or had an indication of THA other than avascular necrosis were excluded. Surgeon factors, assistant factors, and surgical technique were also excluded. We then analyzed the data according to gender, age, BMI, American Society of Anesthesiologists classification, implant fixation type, avascular necrosis stage, proximal femoral morphology, Vancouver classification type, sickle cell type, preoperative hemoglobin (Hb) level, and the risk of periprosthetic fractures. Descriptive statistics were presented using frequency and percentages for categorical variables, and continuous variables were summarized using means ± standard deviations. Independent t-tests and chi-square tests were used to test for associations between categorical variables. At 0.05, the significance level was set. Results Of the patients, 32.7% were male and 67.3% were female. 32.7% of the patients had advanced degenerative changes due to avascular necrosis. Among the patients, 20.4% had an intraoperative periprosthetic femoral fracture, 90% had a Vancouver classification class A, and 10% had a Vancouver classification class B1. According to Dorr classification, 75.5% were classified as Dorr A and 24.5% as Dorr B. Of the patients, 48 had an uncemented implant, and only 1 had cemented. The mean perioperative Hb was 9.02 + 2.02, with a minimum of 6 and a maximum of 14. No significant associations were found between the incidence of intraoperative femoral fracture and the demographic variables and the operative profile characteristics. However, a significantly higher rate of fracture was observed in patients operated on the right side compared to patients operated on the left side. Conclusion The prevalence of periprosthetic intraoperative fracture among SCD patients at King Fahad Hospital Hofuf was 20.4% during the study period. Even with adequate perioperative management, orthopedic surgeons must be prepared to deal with high rates of intraoperative fracture. No significant association was found between the incidence of intraoperative femoral fracture in SCD patients and the demographic variables and the operative profiles. However, a significantly higher rate of fracture was observed in patients operated on the right side compared to patients operated on the left side.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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".

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Citations3
Published2020
Admission routes1
Has abstractyes

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