PURPOSE This investigation examined the trends for gender-based advancement in academic surgery by performing a comparative analysis of the rate of change in the percentage of medical students, surgery residents, and full professors of surgery who are women. METHODS All available Women in Medicine Annual Reports were obtained from the American Association of Medical Colleges (AAMC). The gender compositions of medical graduates, surgery residents, and full professors were plotted. Binomial and linear trendlines were calculated to estimate the year when 50% of surgery full professors would be women. Additionally, the percentage distribution of men and women at each professorial rank was determined from 1995 to 2009 using these reports to demonstrate the rate of academic advancement of each gender. RESULTS The slope of the line of increase for women full professors is significantly less than for female medical students and for female general surgery residents (0.36, compared with 0.75 and 0.99, respectively). This predicts that the earliest time that females will account for 50% of full professors in surgery is the year 2096. When comparing women and men in academic ranks, we find that women are much less likely than men to be full professors. CONCLUSIONS The percentage of full professors in surgery who are women is increasing at a rate disproportionately slower than the increases in female medical students and surgery residents. The rates of increase in female medical students and surgery residents are similar. The disproportionately slow rate of increase in the number of female full professors suggests that multiple factors may be responsible for this discrepancy.
Background SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
Background Approximately 12% of operations for traumatic neuropathy are for patients with segmental nerve loss and less than 50% of these injuries obtain meaningful functional recovery. Polyethylene glycol (PEG) therapy has been shown to improve functional outcomes after nerve severance and we hypothesized this therapy could also benefit nerve autografting. Methods A segmental rat sciatic nerve injury model was used, whereby a 0.5 cm defect was repaired with an autograft using microsurgery. Experimental animals were treated with solutions containing methylene blue (MB) and PEG; control animals did not receive PEG. Compound Actions Potentials (CAPs) were recorded before nerve transection, after solution therapy, and at 72 hours postoperatively. The animals underwent behavioral testing at 24 and 72 hours postoperatively. After sacrifice, nerves were fixed, sectioned, and immunostained to allow for quantitative morphometric analysis. Results The introduction of hydrophilic polymers greatly improved morphological and functional recovery of rat sciatic axons at 1–3 days following nerve autografting. PEG therapy restored CAPs in all animals and CAPs were still present 72 hours postoperatively. No CAPS were detectable in control animals. Footfall asymmetry scores and sciatic functional index scores were significantly improved for PEG therapy group at all time points (p <0.05 and p<0.001; p <0.001 and p <0.01). Sensory and motor axon counts were increased distally in nerves treated with PEG compared to control (p = 0.0189 and p = 0.0032). Conclusions PEG therapy improves early physiologic function, behavioral outcomes, and distal axonal density after nerve autografting.
Patients undergoing microvascular reconstruction are often anemic from a combination of iatrogenic hemodilution and acute blood losses. No major clinical study describes the impact of preoperative anemia on free flap morbidity. The plastic surgery service at a high-volume academic center performed 156 free flaps among 147 patients from December 2005 to December 2010. One hundred thirty-two had a preoperative hemoglobin (Hb) or hematocrit (Hct), with mean values of 11.8±2.4 g/dL and 35.2%±7.0%, respectively. The overall failure rate was 9% (12/132), primarily from vascular thrombosis (6/12). Through logistic regression analysis, Hb and Hct were significant predictors of flap failure (P<0.005) and vascular thrombosis (P<0.05). Fisher exact test revealed a significant increase in failure risk at Hct level less than 30% (Hb<10 g/dL) (relative risk, 4.76, P=0.006), and probit analysis demonstrated an exposure-response relationship to decreased Hct level (P<0.005). These findings support that preoperative anemia could significantly impact free flap morbidity.
During the southern hemisphere winter of 2006 New Zealand experienced a significant increase in the number of reported cases of Campylobacter infection. In total, 112 Campylobacter isolates from eight district health boards (DHBs) located across New Zealand were submitted for PFGE, MLST and Penner serotyping analysis. Distinct clusters of Campylobacter isolates were identified, several of which were composed of isolates from up to five different DHBs located on both the North and South islands of New Zealand. One sequence type, ST-474, was identified in 32 of the 112 isolates and may represent an endemic sequence type present in New Zealand. The spatial pattern of genotypes, combined with the generalized increase in notifications throughout the country is consistent with a common source epidemic, most likely from a source contaminated with the dominant sequence types ST-474 and ST-190 and may also represent widely distributed stable clones present in New Zealand.
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