Impact of Covid-19 on the global orthopaedic research output

Wolf, Milan; Landgraeber, Stefan; Maass, Wolfgang; Orth, Patrick

doi:10.3389/fsurg.2022.962844

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…The time period of this study presents a unique perspective: the COVID-19 pandemic swept across the world, causing lasting effects in all industries. Institutions experienced diminished funding and research output 14 ; orthopaedics had a 5.69% decrease in publications, the largest decline in 2 decades 15 . Simultaneously, orthopaedic case load reduced greatly, with elective surgical procedures decreasing by 80% 16 .…”

Section: Discussionmentioning

confidence: 99%

Retainment of U.S. Orthopaedic Surgeons in Academia from 2016 to 2022

Weintraub,

Ahn,

Herzog

et al. 2024

JBJS Open Access

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Background: Academic medical centers greatly benefit from retaining their physicians; that ensures continuity in patient care, enhances resident education, and maintains a pool of experienced clinicians and researchers. Despite its importance, little research has been published on the retainment of academic faculty in orthopaedics. To address this gap, this study investigates the demographic trends of academic orthopaedic surgeons from 2016 to 2022. By analyzing data pertaining to gender distribution, years of practice, research productivity, and institutional rankings, we aimed to gain insights into the factors influencing faculty retainment, institution changes, and new entrants into academic orthopaedics. Methods: A retrospective cross-sectional analysis of U.S. academic orthopaedic surgeons affiliated with programs under the Accreditation Council for Graduate Medical Education (ACGME) in 2016 and 2022 was performed. Faculty present in both the 2016 and the 2022 data were classified as being “retained” in academia; those present only in 2016, as having “left” academia; and those present only in 2022, as being “new” to academia. The retained group was then divided into movers (those who moved to other institutions) and non-movers. Results: Retained orthopaedists had fewer years of practice, a higher h-index (Hirsch index), and more publications. Non-fellowship-trained orthopaedists had less retainment in academia, and orthopaedists with fellowships in oncology had more retainment in academia. Additionally, movers also had fewer years in practice but an equal level of scholarly productivity when compared with non-movers. Lastly, higher-ranked academic programs retained a greater proportion of orthopaedic surgeons. Conclusions: Over the study period, a majority of orthopaedists (56.99%) chose to remain in academia. Those retained tended to be in the earlier stages of their careers, yet demonstrated higher research output. Notably, the representation of female orthopaedists in academic orthopaedics is on the rise. Conversely, lower-ranked programs faced higher turnover rates, highlighting the challenges that they encounter in retaining faculty members. Clinical Relevance: Academic medical centers benefit from retaining orthopaedic surgeons by maintaining patient relationships, having consistency in resident education, and building on clinical and research expertise. Likewise, orthopaedists benefit from understanding the trends in current academic employment, in order to optimize career planning decisions.

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Section: Discussionmentioning

confidence: 99%

Retainment of U.S. Orthopaedic Surgeons in Academia from 2016 to 2022

Weintraub,

Ahn,

Herzog

et al. 2024

JBJS Open Access

View full text Add to dashboard Cite

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“…There is evidence of worsening patient outcomes and a reduction in trauma cases during the first phase of the Covid pandemic [ 1 , 18 ]. Less obvious changes involve a negative effect on the enrolment of patients in clinical trials [ 19 ] as well as a decrease in publications and scientific output of non-infectiology disciplines [ 20 ]. With the ongoing pandemic, conditions returned to normal [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality

Andonov

Ulm

Graeßner

et al. 2023

BMC Med Inform Decis Mak

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Background Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caused by the Covid-19 pandemic, on model performance. Methods After ethical approval and registration in Clinical Trials (NCT04092933, initial release 17/09/2019), we developed different models for the prediction of perioperative mortality based on preoperative data: one for the pre-pandemic data period until March 2020, one including data before the pandemic and from the first wave until May 2020, and one that covers the complete period before and during the pandemic until October 2021. We applied XGBoost as well as a Deep Learning neural network (DL). Performance metrics of each model during the different pandemic phases were determined, and XGBoost models were analysed for changes in feature importance. Results XGBoost and DL provided similar performance on the pre-pandemic data with respect to area under receiver operating characteristic (AUROC, 0.951 vs. 0.942) and area under precision-recall curve (AUPR, 0.144 vs. 0.187). Validation in patient cohorts of the different pandemic waves showed high fluctuations in performance from both AUROC and AUPR for DL, whereas the XGBoost models seemed more stable. Change in variable frequencies with onset of the pandemic were visible in age, ASA score, and the higher proportion of emergency operations, among others. Age consistently showed the highest information gain. Models based on pre-pandemic data performed worse during the first pandemic wave (AUROC 0.914 for XGBoost and DL) whereas models augmented with data from the first wave lacked performance after the first wave (AUROC 0.907 for XGBoost and 0.747 for DL). The deterioration was also visible in AUPR, which worsened by over 50% in both XGBoost and DL in the first phase after re-training. Conclusions A sudden shift in data impacts model performance. Re-training the model with updated data may cause degradation in predictive accuracy if the changes are only transient. Too early re-training should therefore be avoided, and close model surveillance is necessary.

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