Comparison of long-term clinical and radiological outcomes for cemented keel, cemented peg, and hybrid cage glenoids with anatomical total shoulder arthroplasty using the same humeral component.

Aims: The aim of this study was to longitudinally compare the clinical and radiological outcomes of anatomical total shoulder arthroplasty (aTSA) up to long-term follow-up, when using cemented keel, cemented peg, and hybrid cage peg glenoid components and the same humeral system.

Methods: We retrospectively analyzed a multicentre, international clinical database of a single platform shoulder system to compare the short-, mid-, and long-term clinical outcomes associated with three designs of aTSA glenoid components: 294 cemented keel, 527 cemented peg, and 981 hybrid cage glenoids. Outcomes were evaluated at 4,746 postoperative timepoints for 1,802 primary aTSA, with a mean follow-up of 65 months (24 to 217).

What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Background: Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored.

Questions/purposes: (1) What is the accuracy of machine learning to predict the American Shoulder and Elbow Surgery (ASES), University of California Los Angeles (UCLA), Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation at 1 year, 2 to 3 years, 3 to 5 years, and more than 5 years after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA)? (2) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the minimum clinically important difference (MCID) threshold for each outcome measure? (3) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the substantial clinical benefit threshold for each outcome measure?

Methods: A machine learning analysis was conducted on a database of 7811 patients undergoing shoulder arthroplasty of one prosthesis design to create predictive models for multiple clinical outcome measures.

Are Age and Patient Gender Associated With Different Rates and Magnitudes of Clinical Improvement After Reverse Shoulder Arthroplasty?

Background: An improved understanding of how gender differences and the natural aging process are associated with differences in clinical improvement in outcome metric scores and ROM measurements after reverse total shoulder arthroplasty (rTSA) may help physicians establish more accurate patient expectations for reducing postoperative pain and improving function.

Questions/purposes: (1) Is gender associated with differences in rTSA outcome scores like the Simple Shoulder Test (SST), the UCLA Shoulder score, the American Shoulder and Elbow Surgeons (ASES) Shoulder score, the Constant Shoulder score, and the Shoulder Pain and Disability Index (SPADI) and ROM? (2) Is age associated with differences in rTSA outcome scores and ROM? (3) What factors are associated with the combined interaction effect between age and gender? (4) At what time point during recovery does most clinical improvement occur, and when is full improvement reached?

Methods: We quantified and analyzed the outcomes of 660 patients (424 women and 236 men; average age, 72 ± 8 years; range, 43-95 years) with cuff tear arthropathy or osteoarthritis and rotator cuff tear who were treated with rTSA by 13 shoulder surgeons from a longitudinally maintained international database using a linear mixed effects statistical model to evaluate the relationship between clinical improvements and gender and patient age. We used five outcome scoring metrics and four ROM assessments to evaluate clinical outcome differences.

Quantum dynamics of simultaneously measured non-commuting observables.

In quantum mechanics, measurements cause wavefunction collapse that yields precise outcomes, whereas for non-commuting observables such as position and momentum Heisenberg's uncertainty principle limits the intrinsic precision of a state. Although theoretical work has demonstrated that it should be possible to perform simultaneous non-commuting measurements and has revealed the limits on measurement outcomes, only recently has the dynamics of the quantum state been discussed. To realize this unexplored regime, we simultaneously apply two continuous quantum non-demolition probes of non-commuting observables to a superconducting qubit.