Friends who work in hospitals that simply DO NOT admit in prelabour tell me that women don’t show up until they are in active labour, they don’t come in when they think they ‘might be’ in labour, but when they know they are. Now, I’d like to see the figures from audit, not from anecdote, but they are genuinely incredulous when I tell them about our difficulties in keeping women not in established labour out of the hospital!

I think women do ‘get it’ when you tell them that hospital is not the best place to be in latent phase of labour, you are right – it comes down to education. And the motivation of the caregivers in giving that education.

One of the underlying concepts of a ROC is that there is only one for a given test – it is what it is. When we apply it to something like a decision for cesarean, it gets sort of fuzzy though, as in this case it is more of a metaphorical concept than an actual mathematical formula. The ROC for a analytical test is absolute. The ROC for a intellectual decision is more of a concept than an actual thing.

The best ROCs will hug the upper left corner of the graph, allowing one to have both high sensitivity and specificity at the same time. In that sense, the center curve of the graph is actually the most important, as it is the part that is furthest from that upper left corner. It is this point where the most tradeoff is made between sensitivity and specificity.

>>> In reality, you can get your live baby and live mother discharged home on time with many different approaches to care, but what will fluctuate is the amount of additional risk or minor (often unquantified) injury or waste that you have added to the mix.

The concept of a ROC ignores an individual case – in our metaphor it is not about getting any single baby and mother discharged home. It is about the decision making process in general, and how wherever we choose to set our threshold for making a decision, we will always be trading false positives for false negatives and vice versa.

With that said, I had the exact same reaction as Yehudit when I read this. I think part of the reason we end up on different points along the ROC curve is that we have perceive and measure the trade-offs differently. I recently commented on a post at the Orthopedic Posterous about how we define success. I think it is relevant here because how we define the risks/tradeoffs of a procedure will inform how we define whether or not a procedure has been successful or worthwhile. In reality, you can get your live baby and live mother discharged home on time with many different approaches to care, but what will fluctuate is the amount of additional risk or minor (often unquantified) injury or waste that you have added to the mix. It seems to me, as someone who is no expert in ROC, that the curve is most helpful in the cesarean context at the ends where it hugs the axes, and less relevant in the middle.

Truer words never spoken, my friend.

I’ll just quickly echo what others have said in that all decisions (and medical decisions in particular) involve trade offs. In some cases, the trade offs are clear and the decision is simple. In other cases the trade-offs are complex and well informed people will arrive at drastically different conclusions. That is also fine.

The problem, as you have stated is when we allow our biases to overrule quantifiable decisions and end up with less than optimal choices and actions.

My idea is abstract, but it does help to explain how different people can justifiably have different decision making patterns given the same data. We choose different points on that virtual ROC, as we have different concerns regarding sensitivity and specificity.

Ms Morvay quite correctly stated that we all make our own ROC when we make decisions, and this is constructed not rationally but heuristically. While heuristic thinking allows us to operate quickly in a complex world, it also allows our biases to influence our decision making. This is not to say that this can be avoided, but by being aware of this problem we can often identify such biases prospectively and make better decisions.

I think my “trouble” (if that is the appropriate word for it) stems from the concern that observing where a particular care provider’s decision falls on the curve omits the very pertinent discussion as to where an individual patient’s decision would fall on that same curve. You state the objective nature of this problem succinctly when you say: “Some [midwives and doulas] have tried to make the case that many cesareans are unecessary, and they are of course correct. Some [ob/gyns] have made the case that most cesareans are necessary, and they are correct as well.” To that I would add “some [patients] have tried to make the case that they would rather do X, Y or Z, and they, too are correct.” ( )

I simply am not convinced that science, mathematics and statistics can point us (collectively) to the “right” decision (on the issue of “to cut or not to cut”) every time, because the individual factors in the decision — including the individual mother and the baby — change, by definition, every.single.time.

“If our #1 outcome is to prevent any neonatal injury from intrapartum asphyxia and infection, we could do cesareans for everybody, at the expense of doing many cesareans that were not necessary” I think you gloss over the real contention. The objection is not to “caesareans that were not necessary” (i.e. we have simply performed an unnecessary ‘test’ – like I drew an extra bottle for a blood test at the lab that wasn’t indictaed) but that hiden within that term “unnecessary caesarean” are all the risks for women and babies from caesarean section, that are not balanced by a corresponding benefit.

Sure, there are trade offs between sensitivity and specificity, but these need to be discussed in relation to particular real tests (fetal heartrate monitoring, partograms and definitions of prolonged labour, time from rupture of membranes, maternal risk factors etc…) and not in the abstract as if caesarean section were a “test”.

My take-home from this is that there is a fancy mathematical model to aid decision-making at various cut-off points for a particular treatment/decision/intervention. And that the ideal treatment/decision/intervention would be one that is high in specificity and high in sensitivity, but in real life, you often sacrifice one for the other.

The problem, as you said, is there is that the cut-off for the c-section decision is not well defined, and I have the feeling it may not be easily definable, considering how many factors probably go into a decision that in the end, the doctor probably makes heuristically. Are there any studies that attempt to frame the intervention decision in terms of ROC curves? I’d love to see them.

Thanks again for this post. Learn something new every day.