Morbidité, mortalité et autonomie des patients opérés par prothèse intermédiaire pour fracture cervicale vraie

[Early-onset infection after hemiarthroplasty of the hip: an algorithm for surgical therapy]

BACKGROUND

In a retrospective study, we compared the results of different surgical therapies in 30 patients who had an early-onset infection after implantation of a hip hemiarthroplasty.

MATERIAL AND METHODS

Patients were divided into two groups: In group 1 (n=16, deep infection), we changed the polyethylene inlay as well as the femoral head. In group 2 (n=14, deep infection), an acetabular component was also implanted.

RESULTS

In group 1, successful treatment was achieved in 37.5% of patients 6 months after the last operation, compared with 71.4% in group 2 (p=0.019). All patients were supported with antibiotics. In group 1, three patients died from septic multiorgan failure. Two patients in group 1 and one patient in group 2 received a Girdlestone operation.

CONCLUSIONS

These results suggest that aggressive surgical treatment with soft tissue revision, lavage, and implantation of an acetabular component in combination with antibiotics is a useful technique for treating early-onset infection.

Statistical fallacies in orthopedic research

BACKGROUND

A large number of statistical fallacies occur in medical research literature. These are mostly inadvertent and occur due to lack of understanding of the statistical concepts and terminologies. Many researchers do not fully appreciate the consequence of such fallacies on the credibility of their report.

MATERIALS AND METHODS

This article provides a general review of the issues that could give rise to statistical fallacies with focus on orthopedic research. Some of this is based on real-life literature and some is based on the actual experiences of the author in dealing with medical research over the past three decades. The text is in teaching mode rather than research mode.

RESULTS

Statistical fallacies occur due to inadequate sample that is used for generalized conclusion; incomparable groups presented as comparable; mixing of two or more distinct groups that in fact require separate consideration; misuse of percentages, means and graphs; incomplete reporting that suppresses facts; ignoring reality and depending instead on oversimplification; forgetting baseline values that affect the outcome; misuse of computer packages and use of black-box approach; misuse of P-values that compromises conclusions; confusing correlation with cause-effect; and interpreting statistical significance as medical significance.

CONCLUSION

Mere awareness of the situations where statistical fallacies can occur may be adequate for researchers to sit up and take note while trying to provide a credible report.