Initial treatment of traumatic spinal cord injury remains as controversial in 2023 as it was in the early 19th century, when Sir Astley Cooper and Sir Charles Bell debated the merits or otherwise of surgery to relieve cord compression. There has been a lack of high-class evidence for early surgery, despite which expeditious intervention has become the surgical norm. This evidence deficit has been progressively addressed in the last decade and more modern statistical methods have been used to clarify some of the issues, which is demonstrated by the results of the SCI-POEM trial. However, there has never been a properly conducted trial of surgery versus active conservative care. As a result, it is still not known whether early surgery or active physiological management of the unstable injured spinal cord offers the better chance for recovery. Surgeons who care for patients with traumatic spinal cord injuries in the acute setting should be aware of the arguments on all sides of the debate, a summary of which this annotation presents.

Cite this article: Bone Joint J 2023;105-B(4):347–355.

The importance of registries has been brought into focus by recent UK national reports focusing on implant (Cumberlege) and surgeon (Paterson) performance. National arthroplasty registries provide real-time, real-world information about implant, hospital, and surgeon performance and allow case identification in the event of product recall or adverse surgical outcomes. They are a valuable resource for research and service improvement given the volume of data recorded and the longitunidal nature of data collection. This review discusses the current value of registry data as it relates to both clinical practice and research.

Cite this article: Bone Joint J 2023;105-B(4):356–360.

Economic evaluation provides a framework for assessing the costs and consequences of alternative programmes or interventions. One common vehicle for economic evaluations in the healthcare context is the decision-analytic model, which synthesizes information on parameter inputs (for example, probabilities or costs of clinical events or health states) from multiple sources and requires application of mathematical techniques, usually within a software program. A plethora of decision-analytic modelling-based economic evaluations of orthopaedic interventions have been published in recent years. This annotation outlines a number of issues that can help readers, reviewers, and decision-makers interpret evidence from decision-analytic modelling-based economic evaluations of orthopaedic interventions.

Cite this article: Bone Joint J 2023;105-B(1):17–20.

This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets.

Cite this article: Bone Joint J 2019;101-B:1476–1478

In a systematic review, reports from national registers and clinical studies were identified and analysed with respect to revision rates after joint replacement, which were calculated as revisions per 100 observed component years.

After primary hip replacement, a mean of 1.29 revisions per 100 observed component years was seen. The results after primary total knee replacement are 1.26 revisions per 100 observed component years, and 1.53 after medial unicompartmental replacement. After total ankle replacement a mean of 3.29 revisions per 100 observed component years was seen.

The outcomes of total hip and knee replacement are almost identical. Revision rates of about 6% after five years and 12% after ten years are to be expected.

National registers compare implants by their revision rates, but the validity of the method has never been assessed. The New Zealand Joint Registry publishes clinical outcomes (Oxford knee scores, OKS) alongside revision rates, allowing comparison of the two measurements. In the two types of knee replacement, unicompartmental (UKR) had a better knee score than total replacement (TKR), but the revision rate of the former was nearly three times higher than that of the latter. This was because the sensitivity of the revision rate to clinical failure was different for the two implants. For example, of knees with a very poor outcome (OKS < 20 points), only about 12% of TKRs were revised compared with about 63% of UKRs with similar scores.

Revision therefore is not an objective measurement and should not be used to compare these two types of implant. Furthermore, revision is much less sensitive than the OKS to clinical failure in both types and therefore exaggerates the success of knee replacements, particularly of TKR.

In comparing or assessing methods of treatment it is vital that the appropriate number of patients is selected in order to ensure that the conclusions drawn are statistically viable. This annotation describes the relevance of a statistical power analysis in the context of hypothesis testing to the determination of the optimal sample size of a study. The power of the test indicates how likely it is that the test will correctly produce a statistically significant result