Breastfeeding Part I

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Today we start a two part series on breastfeeding with Dr. Erin Cleary, Assistant Professor of Obstetrics and Gynecology and Clinician Educator at the Warren Alpert Brown School of Medicine. She’s also the incoming MFM fellow at the Ohio State University — so look out for her in July, Buckeye listeners!

Also, thank you Dr. Daniel Ginn, our first Patreon sponsor — and apologies for the dad joke with your name!

We start today with a discussion of the anatomy of the breast, and in particular with lactation. At the bottom of this post is a corresponding Netter image to guide your listening.

The physiology of lactation is somewhat confusing, but in bulleted summary:
Lactogenesis I Early in pregnancy, human placental lactogen, prolactin, and chorionic gonadotropin contribute to maturation of the breast tissue to prepare for lactogenesis.

  • In the second trimester, secretory material which resembles colostrum appears in the glands.  A woman who delivers after 16 weeks gestation can be expected to produce colostrum.

  • Differentiated secretory alveolar cells develop at the ends of the mammary ducts under the influence of prolactin.  Progesterone acts to inhibit milk production during pregnancy. This makes sense from a viewpoint of energy expenditure- grow your baby first in utero, then switch to focus on growing it with milk.

Lactogenesis II is the onset of copious milk production at delivery.  In all mammals, it is associated with a drop in progesterone levels; in humans, this occurs during the 1st 4 days postpartum, with “milk coming in” by day 5

  • During the next 10 days, the milk composition changes to mature milk.  Establishing this supply is Lactogenesis III, and is NOT a hormonally-driven process like Lactogenesis I or II. Rather, this is supply/demand-driven with expression of milk

  • When the milk is not removed, the increased pressure lessens capillary blood flow and inhibits the lactation process.  Lack of sucking stimulation means lack of prolactin release from the pituitary.

Next week, we’ll be back again with Dr. Cleary discussing breastfeeding myths and contraindications, so stay tuned!

Netter’s Anatomy. Copyright Elsevier texts.

Biostatistics Part II

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Welcome back to biostatistics! Today we spend some time on study design and study-specific statistical calculations.

If you have more time, check out the Khan Academy series of videos and infographics on statistics and study design. Their resources are phenomenal and can really help with both understanding CREOG questions as well as helping you out in your own research design!

And for a concise review, check out our own quick notes on the subject.

Biostatistics Part I

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On today’s episode, we try to tackle the highly testable, last-minute-cram topic of biostatistics! This will be the first in a two part series. Sorry about the sound issues — had some problems with Nick’s microphone, but should be fixed after this series!

Below is the official cheat sheet of equations from us for this episode. Hopefully this is helpful in guiding your studying! And stay tuned for next week when we talk more about study design and study-specific statistics.

We also talk about a few other statistical points today:

Prevalence represents the number of people in a population who have a disease. From the above table, this could be calculated as (A+C) / (A+B+C+D).

Likelihood ratio is a value that can represent the significance or utility of a diagnostic test, and is calculated as Sensitivity / 1 - Specificity. In other words, the true positive rate divided by the false positive rate.

An LR > 1 signifies the test is associated with the disease.
An LR < 1 signifies the test is associated with absence of a disease.
An LR that is close to 1 demonstrates the test doesn’t have a strong association with either presence or absence of disease.

Why use LR? If you know the prevalence of disease in a population, you know the pre-test probability of the patient in front of you having the disease. An LR away from 1 demonstrates that your post-test probability is more likely to make you certain of diagnosis. LR of close to 1 doesn’t change your pre-test probability.