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Hi Peace, I just wanted to join in giving you love. Oh boy, have we all made mistakes?And here are some of mine in my first 2 years as an RN just to make you feel better: accidentally weighed a pediatric patient in pounds instead of kilograms for a moderate sedation, and almost double medicated him, thankfully, the doctor (who, by the way, hated me already), found my mistake before we gave the kid the drugs. Underdosed a cardene drip by ten times, thankfully, it was an underdose by ten times and not an overdose because my oncoming nurse found my mistake. Had a really tough peds IV start and finally got labs, but I left the labs in the tube station for 3 hours, and of course they coagulated. Once again the oncoming nurse (who also, by the way, didn't like me, nurses eat their young right?) found my mistake, and unfortunately, the kiddo had to be poked a few more times just to get blood. Got publicly shamed and kicked out of my very first trauma (GSW) because I almost blew up the rapid infuser from the pressure and clamping. I could go on and on and on... Please try to be nice and gracious to yourself. Even if no one else does. It does get easier - you gain lots of muscle memory. You will become an expert, but every nurse, no matter experience, continues to make mistakes. So just try to remember to stay present, double check, use the resources available to prevent unnecessary errors as needed, and avoid practice drift. Hugs to you!!

Hello, As a new grad in the ER and an RN now over 14 yrs, 10 of those ER, here are my recommendations: 1. Every single patient you have, run through the worst case scenario differential diagnoses for their complaints. E.g. lower abd pain has an endless differential but worse case scenario, volvulus, SBO, Abd perf, etc. 2. If you take care of trauma patients, educate yourself on differentials here as well. E.g. blunt force trauma MVC would be pulmonary contusion, cardiac contusion, sternal fx, rib fx, hemo/pneumo, aortic rupture, etc. 3. Get a critical care app that runs through code scenarios to practice 4. Always carry your ACLS algorithm cards on your person until you know them by heart. Also, if you haven't taken ACLS, do that first ? 5. Make sure your rooms are prepared for the worst at the start of your shift. For instance, ensure you have ambu bag (age appropriate size), 02, age appropriate ekg leads, working defib, know where and what's in your code cart ( and HOW to use), exp dates, how to open code drugs quickly and accurately, etc etc. 6. Before an emergency memorize your primary and secondary assessments so that in the emergency you can mentally run through every crit care aspect of the situation, (hemorrhage? Airway (clear, protected?), breathing (even, adequate, 02?), circulation (2 patent IVs, pulses?), etc. 7. Know and memorize your critical care drugs, doses, drip rates Hope this helps you. We are rooting for you

In today's toxic misinformation environment, the old "studies have shown" moniker cannot be used flippantly. Nurses need to have the ability to sift through flawed research studies and find the ones with true merit. The time has come for us to dust off the cobwebs of our knowledge on statistics and be able to say, "Because science!” with conviction. A top priority in nursing is to keep patients informed, and over the last two years, navigating the minefield of misinformation has made this task much more difficult. Therefore, nurses must be well-informed on evidence-based practice and true scholarly research. This article will explain how we as nurses can help our patients, family, and friends detect misinformation in the media. The first step in fighting misinformation is to find articles to back up claims. Any person can say anything on social media, so it's important to teach patients to find the research related to any questionable claims. Below are 5 concepts the astute nurse must grasp to read and understand research articles. Armed with this knowledge, nurses can help patients and family members alike learn to detect misinformation. Statistical Significance Statistical significance means that the probability of the test result happening by chance (pure randomness) is extremely low. Some test results are statistically significant while others are not. Statistical significance is portrayed as a number called a p-value. If the applied independent variable caused enough of an observable change, the study is considered statistically significant. The researcher can then confidently 'reject the null hypothesis' and accept the alternative hypothesis2. Don't worry, there's no need to get bogged down in these semantics to understand basic statistical significance. Chances are, if these terms confuse you, they'll confuse your patients, too. P-value In research articles, a p-value is used to denote statistical significance. A predefined significance level is a requirement of any reputable research study. This is important because if a certain p-value is reached at the end of the study without having set a predefined significance value beforehand, the study results can be unreliable and unproveable. A reasonable p-value, although not used every time, is less than or equal to 0.05. Important to note, p-values can be skewed by random error and systematic errors1. For Example: measuring mid-arm circumference on a small geographical sample size, different people doing the measuring, placement of measurement tape, and how tightly the arm was measured can all contribute to random error1. Systematic errors refer to a broad array of errors, including researcher bias, experience, and incorrect mathematical calculations1. Confidence Interval Confidence Intervals are extremely helpful in research studies. They help tell us whether a study outcome has true merit. The confidence interval is a range and is usually portrayed at the 95 percent level in research studies. Think of it this way: if a p-value tells us 'Yes or no', the confidence interval tells us 'How much'2. Confidence intervals tell the reader, '95% of samples in this study fell within this range'. If a confidence interval falls over the "zero effect" line, the researchers cannot reasonably say that the independent variable had statistically significant effects1. This, in turn, makes the study outcome less useful in clinical practice. For example, if 95 percent of people with documented HTN had a drop in BP after taking a certain antihypertensive in a research study, then a reasonable conclusion would be that this antihypertensive has a reliable effect (statistically significant effect) in dropping BP and may be clinically useful2. On the other hand, if only some of the 95 percent of people in the study had a BP drop while others had either no change or an increased BP, the confidence interval then crosses the "zero effect" line1, and the results cannot be considered statistically significant. Important to note: Strong research studies use both p-values and confidence intervals. Some studies can be statistically significant while having an unreliable confidence interval or vice versa2. These variations can be caused by small sample size or demographics2. Clinical Significance Different from statistical significance, clinical significance is not based on one number (e.g., p value= <0.05). Clinical significance is the presumption that the study outcome would be relevant in a clinical setting1. One way to assess possible clinical significance is by looking at the confidence interval. However, clinical significance also requires some common sense. For instance, using the same BP study from above, let's say that the p-value for the study is <0.05, and the CI does not cross the "zero effect" line, but the mean BP drop is only 2 mmHg. Does a 2 mmHg drop in BP warrant administering this drug instead of another drug? Clinical significance waxes and wanes based on repeat study results, new research, or findings that refute previous results. Hence, the nuances of science are that research study outcomes can change. This fact, while normal, can be frustrating and can grow public distrust. It's important to teach patients and family members this integral aspect of research. Get Comfortable Reading Research! Start getting out there and finding research studies to read. It's a surefire way to get you more comfortable understanding all the terms listed above. A few resources for readers to start delving into research studies are: The Epocrates app- It's free on any app downloading service and has study synopses from the New England Journal of Medicine. UptoDate- If you have access to UptoDate through your workplace, this is a fantastic resource to read new and trending research articles. Google search- Doing a simple Google search on research topics can yield great articles. Just be careful to use reputable websites such as the NCBI, NIH, or PubMed. The more you read, the better prepared you will be to explain these differences to your patients and help mitigate any damage misinformation causes. Happy learning! References 1Statistical significance or clinical significance? A researcher's dilemma for appropriate interpretation of research results 2Confidence Interval or P-Value?

Differential diagnoses: all fever origins such as UTI, Pneumonia, although the lungs sounds we're CTA and good oxygenation, bacteremia, meningitis, gastroenteritis, accidental OD and medication induced hyperthermia (such as risperdal), post ictal from SZ Needs a full w/u: IV, cbc, cmp, crp, procal, blood cx, lactate, CXR, UA, UDS, LP, full med list eval, immunization info, allergy info, maybe a CT or KUB if none of the other imaging studies reveal anything and further evidence of GI/GU sx are found. Immediate treatment includes continuing assess and reeval of airway protection, breathing and oxygenation, antibiotic admin, LP ASAP, and all other mentioned above, orders for admission. If I had to jump right on a Dx I might say meningitis.