Current US Centers for Disease Control and Prevention intramuscular injection needle length guidelines for injection fo the deltoid muscle are based on weight and gender. The aims of this study are ...(1) to evaluate whether other biometric data (age, gender, height, weight and body mass index (BMI)) are better predictors of the thickness of the deltoid subcutaneous fat pad (DSFP) than weight and gender and (2) to evaluate the performance of the CDC weight-based needle length guidelines. This was a retrospective single center cohort study of 386 patients who underwent surveillance PET/CT between 01/01/2020 and 04/01/2021. Patient age, gender, height, weight, BMI and CT measurements of the DSFP were evaluated. DSFP was positively correlated with weight and BMI in men (r = 0.67, P < 0.001; r = 0.74, P < 0.001) and women (r = 0.69, P < 0.001; r = 0.75, P < 0.001) respectively. DSFP was negatively correlated with age in women (r = - 0.19, P = 0.013). Age and BMI were better predictors of DSFP than weight. The best model to predict the DSFP is: Formula: see text A 1-inch needle is expected to reach the deltoid in 85.3% of women less than 200 pounds, and 98.6% of men less than 260 pounds. This rate differed between genders (P < 0.001, odds ratio (OR) 0.08, 95% CI (0.02, 0.29)). A 1.5-inch needle is expected to reach the deltoid in 76.7% of women greater than 200 pounds, and 75.0% of men greater than 260 pounds. Current CDC deltoid intramuscular injection needle length guidelines result in women and obese individuals being more likely to receive subcutaneous injections. Age and BMI based guidelines for needle length selection are more accurate.
To evaluate trends in racial, ethnic, and sex representation at US medical schools across 16 specialties: internal medicine, pediatrics, surgery, psychiatry, radiology, anesthesiology, obstetrics and ...gynecology, neurology, family practice, pathology, emergency medicine, orthopedic surgery, ophthalmology, otolaryngology, physical medicine and rehabilitation, and dermatology. Using a novel, Census-derived statistical measure of diversity, the S-score, we quantified the degree of underrepresentation for racial minority groups and female faculty by rank for assistant, associate, and full professors from 1990-2016.
This longitudinal study of faculty diversity uses data obtained from the American Association of Medical Colleges (AAMC) Faculty Roster from US allopathic medical schools. The proportion of professors of racial minority groups and female faculty by rank was compared to the US population based on data from the US Census Bureau. The Roster includes data on 52,939 clinical medical faculty in 1990, and 129,545 in 2016, at the assistant professor level or higher. The primary measure used in this study was the S-score, a measure of representation based on the probability of the observed frequency of faculty from a racial/ethnic group and sex, given the racial and ethnic distribution of the US. Pearson correlations and 95% confidence intervals for S-score with time were used to measure trends.
Blacks and Hispanics showed statistically significant trends (p<0.05) towards increasing underrepresentation in most specialties and are more underrepresented in 2016 than in 1990 across all ranks and specialties analyzed, except for Black females in obstetrics & gynecology. White females were also underrepresented in many specialties and in a subset of specialties trended toward greater underrepresentation.
Current efforts to improve faculty diversity are inadequate in generating an academic physician workforce that represents the diversity of the US. More aggressive measures for faculty recruitment, retention, and promotion are necessary to reach equity in academia and healthcare.
Embracing Genetic Diversity to Improve Black Health Oni-Orisan, Akinyemi; Mavura, Yusuph; Banda, Yambazi ...
New England journal of medicine/The New England journal of medicine,
2021-Mar-25, Letnik:
384, Številka:
12
Journal Article
To assess whether application of a support vector machine learning algorithm to ancillary data obtained from posterior-anterior dual-energy X-ray absorptiometry (DEXA) studies could identify patients ...with lumbar spine (L1–L4) vertebral body fractures without additional DEXA imaging or radiation. Three hundred seven patients (199 without any fractures of the spine, and 108 patients with at least one fracture of the L1, L2, L3, or L4 vertebral bodies) who had DEXA studies were evaluated. Ancillary data from DEXA output was analyzed. The dataset was split into training (80%) and test (20%) datasets. Support vector machines (SVMs) with 10-fold cross-validation and different kernels were used to identify the best kernel based on the greatest area under the curve (AUC) and the best training vectors in the training dataset. The SVM with the best kernel was then applied to the test dataset to assess the accuracy of the SVM. Receiver operating characteristic (ROC) curves of the SVMs using different kernels in the test dataset were compared using DeLong’s test. The SVM classifier with the linear kernel had the greatest AUC in the training dataset (AUC = 0.9258). The AUC of the SVM classifier with the linear kernel in the test dataset was 0.8963. The SVM classifier with the linear kernel had an overall average accuracy of 91.8% in the test dataset. The sensitivity, specificity, positive predictive value, and negative predictive of the SVM classifier with the linear kernel to detect lumbar spine fractures were 81.8%, 97.4%, 94.7%, and 90.5%, respectively. The SVM classifier with the linear kernel ROC curve had a significantly better AUC than the SVM classifier with the cubic polynomial kernel (
P
= 0.034) for discriminating between patients with lumbar spine fractures and control patients, but not significantly different from the SVM classifier with a radial basis function (RBF) kernel (
P
= 0.317) or the SVM classifier with a sigmoid kernel (
P
= 0.729). All fractures identified by the SVM classifiers were not prospectively identified by the radiologist. SVM analysis of ancillary data obtained from routine DEXA studies can identify lumbar spine fractures without the use of vertebral fracture assessment (VFA) DEXA imaging or radiation, and identify fractures missed by radiologists.
Differences in National Institutes of Health (NIH) funding between specialties may affect research and patient outcomes in specialties that are less well funded.The aim of this study is to evaluate ...how NIH funding has been awarded by medical specialty. This study assesses differences and trends in the amount of funding, by medical specialty, for the years 2011-2020, via a retrospective analysis of data from the NIH RePORTER (Research Portfolio Online Reporting Tools Expenditures and Results).
Longitudinal cross-sectional study SETTING: NIH RePORTER data from 2011 to 2020 for awarded NIH grants (F32, T32, K01, K08, K23, R01, R03, R21, U01, P30) in the following medical specialties: anaesthesiology, dermatology, emergency medicine, family medicine, internal medicine, neurology, neurosurgery, obstetrics and gynaecology, ophthalmology, orthopaedic surgery, otolaryngology, pathology, paediatrics, physical medicine and rehabilitation, plastic surgery, psychiatry, radiation-diagnostic/oncology, surgery, and urology.
NIH grant awardees for the years 2011-2020 INTERVENTION: None PRIMARY AND SECONDARY OUTCOME MEASURES: The following measures were studied: (1) number of grants by specialty, (2) number of grants per active physician in each specialty, (3) total dollar amount of grants by specialty, (4) total dollar amount of grants per active physician in each specialty and (5) mean dollar amount awarded by specialty for each grant type. We investigated whether any of these measures varied between medical specialties.
In general, internal medicine/medicine, psychiatry, paediatrics, pathology and neurology received the most grants per year, had the highest number of grants per active physician, had the highest total amount of funding and had the highest amount of funding per active physician, whereas fields like emergency medicine, plastic surgery, orthopaedics, and obstetrics and gynaecology had the lowest. The mean dollar amount awarded by grant type differed significantly between specialties (p value less than the Bonferroni-corrected alpha=0.00029).
NIH funding varies significantly between medical specialties. This may affect research progress and the careers of scientists and may affect patient outcomes in less well funded specialties.
In vivo micro-Computed Tomography (μCT) is commonly used tool in the study of mouse bone architecture. However, in vivo imaging of mouse cartilage has been limited. Intra-articular contrast injection ...was evaluated for its utility in detecting mouse cartilage in μCT. Clinically used iodinated contrast agent was chosen for its widespread commercial availability. Imaging protocol was developed with wild type C57BL/6 mice for its ability to detect expected cartilage thinning that occurs with sexual maturity. The protocol was then validated with transgenic mouse model with known extracellular matrix loss. μCT findings showed good correspondence with histological assessment. In conclusion, in vivo intra-articular contrast-enhanced μCT arthrography is viable technique for evaluation of mouse cartilage. SUMMARY: In vivo intra-articular contrast enhanced μCT of the mouse knee joint can delineate cartilage thickness and extracellular matrix content. The imaging protocol may be useful for longitudinal evaluation of cartilage anomalies in transgenicmouse model.
Abstract
Objective
Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect ...causal associations among diseases and imaging findings from their co-occurrence in radiology reports.
Materials and Methods
This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P < 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth.
Results
2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold.
Discussion
Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports.
Conclusion
This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.
Objectives
This study aimed to compare the accuracy of PET/CT parameters with CT parameters for directing bone biopsies.
Methods
The study was an IRB-approved retrospective study of 388 patients who ...underwent both 2-
18
F FDG PET/CT and CT within 6 weeks before a bone biopsy. Age, sex, cancer type, lesion length, SUV
max
, tumor to liver (T/L) ratio, CT attenuation, difference in CT attenuation between the lesion and normal bone (delta CT attenuation), and the absolute delta CT attenuation were used as predictors.
T
tests and chi-squared tests were used to compare variables. DeLong’s test was used to compare receiver operator characteristic (ROC) curves.
Results
We reviewed the data from 388 patients. Of these, 295 patients had bone lesion biopsies, and 93 patients had bone marrow aspirations/biopsies. Biopsies of larger bone lesions (
p
= 0.033) and bone lesions with higher SUV
max
(
p
= 0.005) were more likely to show malignancy. For bone lesions, the ROC curve for the SUV
max
(AUC = 0.6827) was better than the ROC curves for delta CT attenuation (AUC = 0.5766,
p
= 0.032) and absolute delta CT attenuation (AUC = 0.5491,
p
= 0.006), but not significantly better than the ROC curves for CT attenuation (AUC = 0.5894,
p
= 0.061) and T/L ratio (AUC = 0.6778,
p
= 0.774). A threshold SUV
max
of 5.25 had an accuracy of 0.713, sensitivity of 0.766, and specificity of 0.549 to predict malignancy in bone lesion biopsies. None of these variables predicted malignancy in bone marrow biopsies (
p
> 0.05 for all).
Conclusions
Metabolic 2-
18
FFDG PET/CT parameters have more clinical impact for planning bone biopsies as compared to CT parameters.
Key Points
• The 2-
18
FFDG PET/CT measurement (SUVmax) has more clinical impact for planning bone biopsies as compared to CT measurements.
• Neither the change in CT attenuation of the lesion relative to normal bone nor the absolute value of this change was a significant predictor of malignancy.
• 2-
18
FFDG PET/CT may have clinical benefit and an additional role in directing bone biopsies.
PDF
![The utility of 2-[18F]FDG-P...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/0938-7994/small "The utility of 2-[18F]FDG-PET/CT maximum SUV versus CT attenuation for directing bone biopsies")