Yam Code
Sign up
Login
New paste
Home
Trending
Archive
English
English
Tiếng Việt
भारत
Sign up
Login
New Paste
Browse
OBJECTIVE To evaluate whether a simple 4-factor model using self-reported data could be used to predict exercise-induced breast pain in elite female athletes. DESIGN Survey study. SETTING Online or hard-copy surveys completed at sporting competitions and training facilities around Australia. PARTICIPANTS Four hundred ninety female athletes competing nationally or internationally across 49 sports. INDEPENDENT VARIABLES A binomial logistic regression analysis was used to evaluate the strength of a predictive model that included 2 continuous independent variables (age and body mass index) and 2 binary independent variables (breast size and sports bra use). Odds ratios were also calculated to determine the likelihood of an athlete reporting exercise-induced breast pain in association with each of the 4 variables. MAIN OUTCOME MEASURES Exercise-induced breast pain was the binary dependent variable. RESULTS The model incorporating athlete age, breast size, body mass index, and sports bra use was found to be statistically significant, but weak, in its ability to predict exercise-induced breast pain in elite female athletes (correctly identified 66% of athletes). For every 1-year increase in age, a significant 2.6% increase in the likelihood of experiencing exercise-induced breast pain was observed. Athletes with medium-to-hypertrophic sized breasts were 5.5 times more likely to experience exercise-induced breast pain than athletes with small breasts. https://www.selleckchem.com/products/cbr-470-1.html CONCLUSIONS Although the current model was not sensitive enough for use by clinicians and coaches, age and breast size were both identified as critical variables in the prediction of exercise-induced breast pain. Future research is encouraged to investigate whether incorporating additional variables such body fat percentage, bra fit, and other relevant factors can add strength to the model.OBJECTIVE To describe the preparticipation examination findings among American athletes by sex, participation level, and age. DESIGN Hypothesis-generating retrospective cohort study. SETTING Saint-Luke's Athletic Heart Center, Kansas City, Missouri. PARTICIPANTS A total of 2954 student athletes. INTERVENTIONS Athletes underwent preparticipation examination, which included history and physical, electrocardiogram, and 2-D transthoracic echocardiogram. MAIN OUTCOME MEASURES Differences noted on screening preparticipation examination by sex, participation level, and age. RESULTS Female athletes reported more symptoms than male athletes (odds ratio [OR] = 1.61; 95% confidence interval [CI], 1.32-1.97; P less then 0.0001) but had lower prevalence of abnormal electrocardiogram (OR 0.52; CI, 0.39-0.68; P less then 0.0001). College athletes reported fewer symptoms than novice athletes (OR 0.35; CI, 0.29-0.43; P less then 0.0001) with no difference in the prevalence of abnormal electrocardiography (ECG) (OR 0.96; CI, 0.73-1.26; P = 0.78). Older athletes reported fewer symptoms than younger athletes (OR 0.61; CI, 0.52-0.71; P less then 0.0001) with no difference in the prevalence of abnormal ECG (OR 1.00; CI, 0.81-1.23; P = 0.89). There were 43 athletes with clinically important findings with no difference in prevalence of these findings across sex, participation level, and age. CONCLUSIONS Among this American cohort of athletes, male athletes reported fewer symptoms and had higher prevalence of abnormal ECG findings compared with female athletes. College and older athletes reported fewer symptoms and had no difference in prevalence of abnormal ECG findings compared with novice and younger athletes, respectively. Despite these differences between groups, the prevalence of clinically important findings was comparable among groups.OBJECTIVE To investigate CrossFit-related injuries presenting to a pediatric sports medicine clinic. DESIGN Retrospective review of pediatric CrossFit-related injuries from between January 1, 2003, and June 31, 2016. SETTING Pediatric sports medicine clinic at a tertiary-level academic medical center. PATIENTS Patients with injury related to CrossFit participation. INDEPENDENT VARIABLES Sex, age, injury site, diagnosis, diagnostic imaging, and treatment. MAIN OUTCOME MEASURES Annual CrossFit-related injury proportion (%) over time. RESULTS One hundred fifteen medical identified (N = 55 female; mean age, 25.2 ± 10.4 years). Proportion of CrossFit-related injuries presenting to clinic relative to overall clinic volume consistently increased over time (Pearson r = 0.825; P = 0.022). Injury location included head (0.08%), trunk/spine (25.2%), upper extremity (27.0%), and lower extremity (47.0%). Common injured joints included knee (27%), spine (24.3%), and shoulder (16.5%). Nearly half of patients had a single diagnostic imaging (49.6%; 57 of 115). Most common diagnostics included magnetic resonance imaging (60.0%; 69 of 115), plain radiographs (51.3%; 59 of 115), ultrasound (10.4%; 12 of 115), and computerized tomographic scan (9.6%; 11 of 115). Most commonly prescribed treatments included physical/occupational therapy (38.3%; 44 of 115), activity modification (19.1%; 22 of 115), crutches/brace/splinting/compression sleeve (13.0%; 15 of 115), and non-steroidal anti-inflammatory medications (10.4%; 12 of 115). CONCLUSIONS CrossFit-related injury proportion presenting to a pediatric sports medicine clinic increased over time. A notable proportion of injuries occurred to the trunk and spine. Advanced imaging was obtained in approximately half of these youth athletes. Further research in youth CrossFit athletes is required surrounding mechanism of injury to prevent future injury in this mode of training for youth athletes.INTRODUCTION Recent studies in general surgery and internal medicine have shown that female physicians may have improved morbidity and mortality compared with their male counterparts. In the field of orthopaedic surgery, little is known about the influence of surgeon gender on patient complications. This study investigates patient complications after hip and knee arthroplasty based on the gender of the treating surgeon. METHODS Using a risk-adjusted outcomes database of 100% Medicare data from a third party, an analysis of outcomes after primary hip and knee arthroplasty based on surgeon gender was performed. This data set, which provided risk-adjusted complication rates for each surgeon performing at least 20 primary knee or hip arthroplasties from 2009 to 2013, was matched with publically available Medicare data sets to determine surgeon gender, year of graduation, area of practice, and surgical volume. Confounding variables were controlled for in multivariate analysis. RESULTS Of the 8,965 surgeons with identified gender, 187 (2.
Paste Settings
Paste Title :
[Optional]
Paste Folder :
[Optional]
Select
Syntax Highlighting :
[Optional]
Select
Markup
CSS
JavaScript
Bash
C
C#
C++
Java
JSON
Lua
Plaintext
C-like
ABAP
ActionScript
Ada
Apache Configuration
APL
AppleScript
Arduino
ARFF
AsciiDoc
6502 Assembly
ASP.NET (C#)
AutoHotKey
AutoIt
Basic
Batch
Bison
Brainfuck
Bro
CoffeeScript
Clojure
Crystal
Content-Security-Policy
CSS Extras
D
Dart
Diff
Django/Jinja2
Docker
Eiffel
Elixir
Elm
ERB
Erlang
F#
Flow
Fortran
GEDCOM
Gherkin
Git
GLSL
GameMaker Language
Go
GraphQL
Groovy
Haml
Handlebars
Haskell
Haxe
HTTP
HTTP Public-Key-Pins
HTTP Strict-Transport-Security
IchigoJam
Icon
Inform 7
INI
IO
J
Jolie
Julia
Keyman
Kotlin
LaTeX
Less
Liquid
Lisp
LiveScript
LOLCODE
Makefile
Markdown
Markup templating
MATLAB
MEL
Mizar
Monkey
N4JS
NASM
nginx
Nim
Nix
NSIS
Objective-C
OCaml
OpenCL
Oz
PARI/GP
Parser
Pascal
Perl
PHP
PHP Extras
PL/SQL
PowerShell
Processing
Prolog
.properties
Protocol Buffers
Pug
Puppet
Pure
Python
Q (kdb+ database)
Qore
R
React JSX
React TSX
Ren'py
Reason
reST (reStructuredText)
Rip
Roboconf
Ruby
Rust
SAS
Sass (Sass)
Sass (Scss)
Scala
Scheme
Smalltalk
Smarty
SQL
Soy (Closure Template)
Stylus
Swift
TAP
Tcl
Textile
Template Toolkit 2
Twig
TypeScript
VB.Net
Velocity
Verilog
VHDL
vim
Visual Basic
WebAssembly
Wiki markup
Xeora
Xojo (REALbasic)
XQuery
YAML
HTML
Paste Expiration :
[Optional]
Never
Self Destroy
10 Minutes
1 Hour
1 Day
1 Week
2 Weeks
1 Month
6 Months
1 Year
Paste Status :
[Optional]
Public
Unlisted
Private (members only)
Password :
[Optional]
Description:
[Optional]
Tags:
[Optional]
Encrypt Paste
(
?
)
Create New Paste
You are currently not logged in, this means you can not edit or delete anything you paste.
Sign Up
or
Login
Site Languages
×
English
Tiếng Việt
भारत