The Linda Crane Lecture is now available on the Cardiopulmonary Physical Therapy Journal at this link – the publisher has made this free accessible. I’m interested in people’s thoughts. It was harder than I thought to turn these ideas into a paper from a lecture, much is lost with the absence of emotion. That is not something I am happy about (no pun intended), my preference would be to have ideas that stand based on reason – meaning, there is much more to do for these ideas to have a reasonable chance.


Pivot and Leverage – points on a graph, points for clinical consideration

Pivot and Leverage Points are some of the concepts for clinical cases that are widespread and implicitly well understood, but not necessarily explicitly described well or discussed. Giving them names in the PSU DPT program has allowed us to explicitly use the concepts in case discussions. Being able to share ideas with a language that is useful is such an important component in education and practice.

In general

You don’t need to start a case knowing pivot or leverage points. But most cases have them and it is a bit useful if you can identify them when they play an important role in decision-making.

Usually we distinguish different causes of symptoms by examining signs (including the results of clinical tests and special tests), where the results of these tests essentially “pivot” you from cause to another. It is sometimes helpful to think about cases as having different underlying physiological, anatomical, mechanical situations that warrant different intervention approaches with the reasons to believe one of these cases may benefit greatly from a particular “leveraging” approach.

Pivot Point

A pivot point is a piece (or set) of information (examination findings, etc.) that changes the direction of the examination. Yes, having a positive test and several indications of a DVT would change the direction of an examination of LE pain. It is based on “abduction” – that is making an inference to the most probable cause based on the observed effects. It is essentially what we do when we attempt to identify the cause of a problem so we can reason about how to intervene.

Pivot points involve a test (or set of tests) that pivot us by providing evidence that rules out one cause (lower (-) likelihood ratios which are associated with higher sensitivities) and rules in another cause (higher (+) likelihood ratios that are associated with higher specificities).

In the figure below, Test 1 is positive with Cause 1 and negative otherwise; Test 2 is positive with Cause 2 and negative otherwise; Test 3 is positive with Cause 3 and negative otherwise. So the results of these 3 tests pivot us between these three possible causes. Of course, the causes are not mutually exclusive so it is a bit more complicated than my model here.



Leverage Point

A leverage point is a point that if changed we believe will greatly change the status of the system moving forward (movement system, functional system, etc). When an intervention works on a leverage point, just like a lever, there is a greater output than expected based on the input.

There at least two ways that something can be a leverage point.

  1. Something wrong with the point and it is easily fixed
  2. The point is a “hub” node, or has an impact on several parts of the system

In the below figure any node leading toward functional movement could be a leverage based on the first way. For example, if joint arthrokinematic movement is impaired and it can be easily fixed it may have a large leveraging effect.

Whereas it takes a node such as Nerve Root Integrity to be a leverage of type 2. As is clear, nerve root integrity impacts several other variables. So, if it is impaired and can be fixed the results are significant since they are leveraged through the system.


The 2018 Linda Crane Lecture: Synthesis, Causal Models, Causal Knowledge

Last week at the APTA Combined Sections Meeting in New Orleans I had the distinct and humbling experience of receiving the Linda Crane Lecture Award. This was an opportunity provided by my nominators and the Cardiovascular and Pulmonary Section of the APTA based on having made outstanding and enduring contributions to the practice of physical therapy as exemplified in the professional career of Dr. Linda Crane.

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This lecture will be published – along with all the other Crane Lectures – in the Cardiopulmonary Physical Therapy Journal.

My talk was about 75 minutes long and essentially presented what I have been working on for the past 13 years, and have been disseminating in a variety of forms for the past 3 years (this blog, presentations, editorials and yes, the development of a new DPT program at Plymouth State University).

To this date people ask me where the heck the PSU DPT philosophy comes from – and with this blog, the Crane Lecture and its subsequent publication the philosophy should make more sense:

“The Department embraces a critical realist philosophy of science and its associated consequences that ontology determines epistemology, and the stratification of reality. We fully support the use of evidence-based empirical observations in the development of knowledge, and the subsequent rational development of knowledge for use in practice, a framework we describe as knowledge-based practice. We believe the best representations of knowledge for practice are causal structures, including models and networks from which logical inferential rules can be applied in clinical decision-making.”

Of course, in the coming years the full faculty at PSU DPT will have to vote on this philosophy – it was rather easy to get the full faculty to agree with it when the full faculty back in June 2015 was the one person that wrote it!

For now, I have posted the lecture (LindaCraneLecture2018_Collins) I gave (well how I got started), and the powerpoint I used (LindaCrane2018_CollinsPPT).

Screen Shot 2018-02-28 at 10.51.30 AM

I also thought I would include the very well written summary by Lois Douthitt from APTA on the talk. She does a great job of capturing the essence of the talk and argument.



Prediction without causation is wishful thinking

After months of slumber the KBP blog has been awoken. The slumber was not a true slumber, the author has been otherwise engaged in a variety of activities keeping him (me) from writing. A series of events have been pushing me back toward posting again.

  1. APTA Education Leadership Conference presentation with my colleague Kelly Legacy called “Making Causal Inferences Explicit” – at some point soon I will voice over that talk and post (or just post the slides)
  2. An advance release of a paper about causal mechanisms was released to be published in PTJ – that will certainly be worthy of a post
  3. Engagement with the wonderful folks as APHPT and was just recently interviewed by their founder / president Mike Eisenhart. It was a great time and made me realize that some of this stuff I have been blogging about could be useful, so I may not want to throw in the towel just yet.

But, what has inspired me just this moment, is a PT In Motion article about an advance release of a paper coming out in the journal “Arthritis Care and Research” called: “Minimum performance on clinical tests of physical function to predict walking 6000 steps/day in knee osteoarthritis: An observational study“.

The take home message here will be that prediction without causation is wishful thinking. It is possible. Yes, it is true, data the establishes an association can then be used to predict. However, without causation the direction of the events are not known and our attempt to use prediction for intervention is left as wishful thinking. As an extreme example, if we demonstrated an association between a herniated disk and radicular pain then do we believe that removing the radicular pain through cryotherapy or medications or hypnosis makes the herniated disk go away? No. We believe that there is a causal association between the herniated disk and radicular pain, and while all of those mechanisms to intervene on radicular pain may have their place (I am neither recommending nor advocating), we are more comfortable that if the herniated disk was intervened on and went away that the radicular pain would go away (I do realize it is more complex than this, that there are other causes of radicular pain, etc).

To get back to the study at hand.

The purpose of this study was to identify minimum performance thresholds on clinical tests of physical function predictive to walk ≥6000 steps/day.

Methods: Using data from the Osteoarthritis Initiative, we quantified daily walking as average steps/day from an accelerometer (Actigraph GTM1) worn for >10 hours/day over one week. Physical function was quantified using three performance-based clinical tests: five times sit to stand test, walking speed (tested over 20 meters) and 400-meter walk test. To identify minimum performance thresholds for daily walking, we calculated physical function values corresponding to high specificity (80 to 95%) to predict walking ≥6000 steps/day.

Results: Among 1925 participants (age [mean±sd] 65.1±9.1 years, BMI 28.4±4.8 kg/m2, 55% female) with valid accelerometer data, 54.9% walked ≥6000 steps/day. High specificity thresholds of physical function for walking ≥6000 steps/day ranged from 11.4 to 14.0 sec on the five times sit to stand test, 1.13 to 1.26 meters/sec for walking speed, or 315 to 349 sec on the 400-meter walk test.

Conclusion: Not meeting these minimum performance thresholds on clinical tests of physical function may indicate inadequate physical ability to walk ≥6000 steps/day for people with knee OA. Rehabilitation may be indicated to address underlying impairments limiting physical function.

Emphasis has been added. It is an observational study. Data collected was cross-sectional. In the discussion about limitations the authors casually admit:

Third, the cross-sectional design allowed us to identify a relationship between physical function and physical activity, but not to draw conclusions about causation.

This is the third of four limitations which are mentioned. Of course, like a lot of studies, the limitations are mentioned but the implications of the limitations are not discussed.

In this study data was collected on performance (3 tests that are reasonable to expect to be associated with walking) and walking (as number of steps per day). Despite the data being collected at the same time (cross-sectionally) and the admitted limitations in drawing causal conclusions the study still moves ahead with the following causal assumption:


This seems perfectly reasonable. But so isn’t this:

dagitty-model (1).png

The problem here is that this study did not explicitly test either of these assumptions, yet its conclusion is completely committed to the first assumption.

Not meeting these minimum performance thresholds on clinical tests of physical function may indicate inadequate physical ability to walk ≥6000 steps/day for people with knee OA. Rehabilitation may be indicated to address underlying impairments limiting physical function.

It is equally valid to have concluded:

Not walking ≥6000 steps/day may indicate these minimum performance thresholds on clinical tests of physical function for people with knee OA. Walking ≥6000 steps/day may be indicated to address minimum performance thresholds on clinical tests of physical function.

In fact, there is another hidden assumption. They have assumed that if someone does not walk ≥6000 steps/day that they cannot walk ≥6000 steps/day. This is a major assumption that has a huge impact on the conclusions drawn from this study.

In the end, this is a valuable contribution. It contributes to an understanding that can be built on with further work. But it does not allow prediction of the capability of walking ≥6000 steps/day. It maybe allows prediction of actually walking ≥6000 steps/day; BUT if we start with walking ≥6000 steps/day, it allows prediction of physical test performance. But these examples of prediction are based on associations. And predictions based on associations are less useful for clinical interventions.

But please keep in mind, that predication without causation is simply wishful thinking.


Differential diagnosis project update and presentation

Had a chance yesterday to share the last developments in the quest to improve differential diagnosis in physical therapy through the use of causal models and Bayesian networks.

The presentation is online here (I plan to add voice over and break it up into smaller online webinar based sessions soon):

All the files for it – including the example networks built with samiam and full conditional probability tables (estimates, but at least these assumptions are explicit and shared) are on GitHub:

The most amazing part of the presentation to me was that with 41 therapists in the room; no one challenged my repeated statements that all of their decision making is founded on probabilistic causal inferences within complex causal networks. In practice they simply do not consider the underlying assumptions and probabilities, but when they make a decision they are making a statement about what they believe, and therefore about a probability. There seemed to be general ascent on this claim.

Thank you to all of you that attended – I look forward to more work and interaction with collaborators as the project continues to unfold; and to offering additional continuing education and even training opportunities for anyone interested.

The general premises of the project are:

– Clinical practice situations are complex systems (see this post)
– Complex systems include high dimensional, multi – variable causal networks.
– Reasoning with such systems is challenging and often includes several hidden assumptions.
– Both for teaching and for research aimed to improve the decision making from such reasoning, it is valuable to make as many of the previously hidden assumptions explicit.
– Existing methods to attempt to improve differential diagnosis (use of diagnostic accuracy such as sensitivity, specificity, likelihood ratios) don’t go far enough. They are far too simple compared to actual complexity of actual clinical practice situations.
– Bayesian networks offer – for clinical educational and clinical research purposes – a bridge between the highly complex system of actual clinical practice and existing methods that attempt to improve the process.
– Clinical education and clinical research support clinical practice and are necessary components to the system attempting to improve practice, which is something that providers, clients and payors are all seeking.

Screen shot of a sample samiam model for back pain with several clinical signs observed and instantiated leading to adjusted posterior probabilities:

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The structure of structures: anatomical networks in PT anatomy education

A few months ago I started thinking about what anatomy, as foundational knowledge for physical therapy practice, would look like and how it would be taught in a program considering “knowledge based practice: cause, models and inference” as a clinical epistemology (here). Now I am taking that a step further, in a KBP – concept based curriculum – with core movement concepts including causation, adaptation and systems.

It is easy to see how “cause” is important in the study of anatomy. Cause is, after all, how we use anatomical knowledge in practice. Another way to say this (flipped), is that anatomical knowledge is one set of knowledge from which we reason causally. When using abduction to generate a list of possible “causes” of a set of signs and/or symptoms we generally consider the anatomy: “moving the leg like that could be due to a tight X, or a weak Y, or a stiff Z, …, etc.”

It is also easy to see how adaptation is important in the study of anatomy. After all, all of our current adult anatomical musculoskeletal forms are, within a set of boundary constraints, the result of the sum of adaptations of our life long journey to where we are right now. So how we live is predictive of our future anatomy (causal induction and causal deduction), and our current anatomy testifies to the journey we have taken (causal abduction).

Now how about a system? It is certainly easy to talk about the “systems” of our body anatomically. After all, we learn our anatomy in A&P I and II as a set of systems. This is how A&P books are divided. It is a fine way to divide them. Then we get into gross anatomy and we divide up the body regionally – again a fine way to divide up the body to learn it. But what about learning about the system of the system? For example, the system of the skeletal system.Or the system of the muscular system. Or the system of the musculoskeletal system. There is a modularity balanced with integration in all of these (and other) systems.

Well, this is what we can learn from a rather new application of an emerging analytic approach. The application of network analysis (studying systems by their network structure using graph theory (logical, mathematical), and by the way, for those of you that have been reading along with this blog – a “graphical causal model” is simply a network, a particular type of network, a DAG (directed acyclic graph)). This new application to anatomy has been termed: Anatomical Network Analysis (AnNA).

AnNA is exactly what these authors (Diogo R, Esteve-Altava B, Smith C, Boughner JC, Rasskin-Gutman D) have been doing for the past few years:

AnNA is original, insightful and very useful.  Making this particular paper even more useful, the authors have subscribed fully to reproducible research, so their supplements, data, details regarding the analysis including R code – and a vast set of networks – is available on FigShare (here).

The paper citation is:

Article Source: Anatomical Network Comparison of Human Upper and Lower, Newborn and Adult, and Normal and Abnormal Limbs, with Notes on Development, Pathology and Limb Serial Homology vs. Homoplasy
Diogo R, Esteve-Altava B, Smith C, Boughner JC, Rasskin-Gutman D (2015) Anatomical Network Comparison of Human Upper and Lower, Newborn and Adult, and Normal and Abnormal Limbs, with Notes on Development, Pathology and Limb Serial Homology vs. Homoplasy. PLOS ONE 10(10): e0140030. doi: 10.1371/journal.pone.0140030

And I hope it is ok – but here are the first two figures in an attempt to encourage readers to get the paper:

Figure 1:

Figure 2:


Needless to say, I strongly believe that Anatomical Network Analysis (AnNA) will provide many benefits to the education of anatomy (in addition to the benefits discussed by the authors in this paper (and their other papers)), and potentially to the physical therapy profession as a whole as we work together on the human movement system. I am looking forward to fully exploring the possibilities and implementing an anatomy course and research that utilizes the concepts that emerge (pun intended), when we consider the network structure of the structures we use in practice. What it tells about about anatomical causal associations, what it tells us about anatomical adaption, and what it tells about about the system of anatomical systems with implications for movement.

As a final word – one of the authors on this paper also has a new anatomy book out – from what I have read so far, it is highly recommended (and will be required for PSU – DPT students).

51FtN42PRkL._SX348_BO1,204,203,200_.jpgDIOGO, R., D. NODEN, C. M. SMITH, J. A. MOLNAR, J. BOUGHNER, C. BARROCAS & J. BRUNO (2016). Learning and understanding human anatomy and pathology: an evolutionary and developmental guide for medical students. Taylor & Francis (Oxford, UK). 348 pages.








It’s great to be back blogging – it has been a long 18 months getting the PSU-DPT program into the “candidacy” pre-accreditation phase. But now as we look to accept our first class an implement this new program I look forward to blogging more as things unfold.

Randomized controlled trials – a KBP perspective

Re blogging this today in light of a discussion regarding this paper:

Morris PE, Berry MJ, Files D, et al. Standardized Rehabilitation and Hospital Length of Stay Among Patients With Acute Respiratory Failure: A Randomized Clinical Trial. JAMA. 2016;315(24):2694-2702. doi:10.1001/jama.2016.7201.


Knowledge Based Practice

In evidence based practice randomized controlled trials (RCTs) have a very high standing. In fact, by the GRADE approach to weighing evidence for a clinical practice guideline (CPG) a single, large, well conducted RCT can result in an evidence rating as high as a systematic review of RCTs. This post is not an attempt to argue against the use of RCTs to develop knowledge for practice. The purpose is to simply share some thoughts about the limitations of RCTs and what KBP suggests for RCT method0logy planning.

RCTs are highly regarded because when they demonstrate an effect with a large sample then the cause tested is the most likely explanation for the observed effect (low risk of bias in well designed, large sample RCTs). There will be variability in the effect and the amount of variability is important to consider for the clinician as the patients you treat are not the…

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