Alumni of Note

Key Player - Maryland Stadium Authority\u27s Alison Asti; Big Hitters: Percival\u27s Winning Softball Team; Fitting Tributes: Newly Endowed Scholarships

Reducing variability in apparent operative inclination during total hip arthroplasty:findings of a randomised controlled trial

Aims: To determine which of 3 methods of cup insertion most accurately achieved a target apparent operative inclination (AOI) of 35° ± 2.5°: (1) Freehand; (2) Modified Mechanical Alignment Guide (MAG); or (3) Digital Inclinometer assisted. Methods: Using a cementless cup via a posterior approach in lateral decubitus 270 participants were recruited, with 90 randomised to each method. The primary outcome was the unsigned deviation from target AOI. The digital inclinometer was used to measure AOI in all cases, though the surgeon remained blinded to the reading intraoperatively for both the Freehand and MAG methods. Results: Mean deviation from target AOI for the Freehand, Modified 35° MAG and Digital Inclinometer techniques was 2.9°, 1.8° and 1.3° respectively. When comparing mean deviation from target AOI, statistically significant differences between the Freehand / Inclinometer groups (p &lt; 0.001), the Freehand / Modified 35° MAG groups (p &lt; 0.001) and the Digital Inclinometer / Modified 35° MAG groups (p &lt; 0.023) were evident. The Digital Inclinometer technique enabled the surgeon to achieve a target AOI of 35° ± 2.5° in 88% of cases, compared to 71% of Modified 35° MAG cases and only 51% of Freehand cases. Discussion: The Digital Inclinometer and the Modified 35° MAG techniques were both more accurate than the Freehand technique, with the Digital Inclinometer technique proving most accurate overall. Radiographic inclination (RI) is also influenced by operative anteversion; however, the greatest source of error with respect to RI occurs when the pelvic sagittal plane is not horizontal at the time of acetabular component insertion. Clinical Trial Protocol number: NCT01831401.</p

Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts

Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs are 0-shot adapted to a downstream dataset by designing prompts that are relevant to the dataset. Such prompt engineering makes use of domain expertise and a validation dataset. Meanwhile, recent developments in generative pretrained models like GPT-4 mean they can be used as advanced internet search tools. They can also be manipulated to provide visual information in any structure. In this work, we show that GPT-4 can be used to generate text that is visually descriptive and how this can be used to adapt CLIP to downstream tasks. We show considerable improvements in 0-shot transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD (~7%), SUN397 (~4.6%), and CUB (~3.3%) when compared to CLIP's default prompt. We also design a simple few-shot adapter that learns to choose the best possible sentences to construct generalizable classifiers that outperform the recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized fine-grained datasets. We will release the code, prompts, and auxiliary text dataset upon acceptance.Comment: 10 pages, Pre-prin

Reducing variability in apparent operative inclination during total hip arthroplasty:findings of a randomised controlled trial

Aims: To determine which of 3 methods of cup insertion most accurately achieved a target apparent operative inclination (AOI) of 35° ± 2.5°: (1) Freehand; (2) Modified Mechanical Alignment Guide (MAG); or (3) Digital Inclinometer assisted. Methods: Using a cementless cup via a posterior approach in lateral decubitus 270 participants were recruited, with 90 randomised to each method. The primary outcome was the unsigned deviation from target AOI. The digital inclinometer was used to measure AOI in all cases, though the surgeon remained blinded to the reading intraoperatively for both the Freehand and MAG methods. Results: Mean deviation from target AOI for the Freehand, Modified 35° MAG and Digital Inclinometer techniques was 2.9°, 1.8° and 1.3° respectively. When comparing mean deviation from target AOI, statistically significant differences between the Freehand / Inclinometer groups (p &lt; 0.001), the Freehand / Modified 35° MAG groups (p &lt; 0.001) and the Digital Inclinometer / Modified 35° MAG groups (p &lt; 0.023) were evident. The Digital Inclinometer technique enabled the surgeon to achieve a target AOI of 35° ± 2.5° in 88% of cases, compared to 71% of Modified 35° MAG cases and only 51% of Freehand cases. Discussion: The Digital Inclinometer and the Modified 35° MAG techniques were both more accurate than the Freehand technique, with the Digital Inclinometer technique proving most accurate overall. Radiographic inclination (RI) is also influenced by operative anteversion; however, the greatest source of error with respect to RI occurs when the pelvic sagittal plane is not horizontal at the time of acetabular component insertion. Clinical Trial Protocol number: NCT01831401.</p

Enhancing clip with gpt-4: harnessing visual descriptions as prompts

Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs are 0-shot adapted to a downstream dataset by designing prompts that are relevant to the dataset. Such prompt engineering makes use of domain expertise and a validation dataset. Meanwhile, recent developments in generative pretrained models like GPT-4 mean they can be used as advanced internet search tools. They can also be manipulated to provide visual information in any structure. In this work, we show that GPT-4 can be used to generate text that is visually descriptive and how this can be used to adapt CLIP to downstream tasks. We show considerable improvements in 0-shot transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD (~7%), SUN397 (~4.6%), and CUB (~3.3%) when compared to CLIP's default prompt. We also design a simple few-shot adapter that learns to choose the best possible sentences to construct generalizable classifiers that outperform the recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized fine-grained datasets. The code, prompts, and auxiliary text dataset is available at this https URL

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Background: Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. Objective: This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. Methods: Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score Results: In the classification of a low RBANS score ( Conclusions: The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients

Advances in quadrupole and time-of-flight mass spectrometry for peptide MRM based translational research analysis

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The application of unit resolution tandem quadrupole and high-resolution orthogonal acceleration ToF mass spectrometers for the quantitation and translational analysis of proteolytic peptides is described. The MS platforms were contrasted in terms of sensitivity and linear response. Moreover, the selectivity of the platforms was investigated and the effect on quantitative precision studied. Chromatographic LC conditions, including gradient length and configuration, were investigated with respect to speed/throughput, while minimizing isobaric interferences, thereby providing information with regard to practical sample cohort size limitations of LC-MS for large cohort experiments. In addition to these fundamental analytical performance metrics, precision and linear dynamic ranges were also studied. An LC-MS configuration that encompasses the best combination of throughput and analytical accuracy for translational studies was chosen, despite the MS platforms giving similar quantitative performance, and instances were identified where alternative combinations were found to be beneficial. This configuration was utilized to demonstrate that proteolytically digested nondepleted samples from heart failure patients could be classified with good discriminative power using a subset of proteins previously suggested as candidate biomarkers for cardiovascular diseases