And I thought we were done with the mid-shaft clavicle….

Bicycle
Bicycle injuries are by far the most common mechanism of injury for clavicle injuries. The image is CC by Hiroyuki Takeda.

So… just a few days after my previous clavicle post, Ahrens et al released their multi-center study on 300 patients randomized to surgery. They found that operated clavicles have less pain early on, but that after 9 months they perform the same. The study was excellently performed with 20 centers, adequate patient selection, random block permutation for treatment allocation, and reasonable treatment options. Continue reading

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The clavicle fracture – can the madness finally come to an end?

Finding the right path can be harder than we want to think . The image is CC by Gwenole Camus.

In the early 2000s Nowak et al. [1] shattered the belief that mid-shaft clavicle fractures always healed fine; even after 10 years almost half of the patients had remaining symptoms*. This common injury had also by in others [2] (with shorter follow-ups) been hinted as problematic, and within short we were showered with fancy new implants. After ≥ 7 RCTs [3], [4] on the subjects it seems that these new implants failed do deliver. Can we finally start questioning if surgery is the solution? Continue reading

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Our AI on par with humans?

The first step in orthopedic deep learning. The image is CC from Pixabay.

We finally published our first article on deep learning (a form of artificial intelligence, AI) in orthopedics! We got standard off-the-shelf neural networks to perform equally well as senior orthopedic surgeons for identifying fractures. This was under the premise that both the network and the surgeons reviewed the same down-scaled images. Nevertheless, this was better than we expected and verifies my belief that deep learning is suitable for analyzing orthopedic radiographs. Continue reading

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ASA as DVT prophylaxis gaining in popularity?

Keeping the blood flowing is part of core medical knowledge – then why the controversy? The image is CC by Andi Campbell-Jones

In a recent post I noted that there was a dissonance between what I’ve been taught in school and what is actually the case regarding thrombosis prophylaxis after orthopaedic surgery. A new study by Parvizi et. al. looks into different dosages of ASA as a thromboprophylaxis after joint arthroplasties. Coming from a country that has fully embraced LMWH this feels alien… regardless, there seems to be increasing evidence that challenges my point of view. Continue reading

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Unstable ankle fractures – a British multi-center study provides a neat alternative to surgery

Large multi-center RCTs are worth celebrating with fireworks. The image is CC by Colin Knowles.

The Brits have done it again: an amazing, multi-center study on ankle surgery. They looked at 620 unstable ankle fractures and compared close contact casting (CCC) with surgery. Like so many orthopaedic interventions it seems that both methods are equivalent regarding patient reported outcome although 15% had malunion vs 3% in in the surgical group. Likewise there was a higher non-union in the CCC group, 10% vs 3%. Furthermore about 1 in 5 in the CCC group required later surgery. Continue reading

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Chochrane supports restrictive transfusions

Will Cochrane break through to the blood thirsty colleagues? The image is CC by Gaviota Paseandera.
Will Cochrane break through to the blood thirsty colleagues? The image is CC by Gaviota Paseandera.

I’ve previously written a two posts on blood transfusions from a surgeons perspective (End of the blood reign and A bloody mess) and I was therefore thrilled when I stumbled upon this [Cochrane review](https://www.ncbi.nlm.nih.gov/pubmed/27731885) that concludes:

The findings provide good evidence that transfusions with allogeneic RBCs can be avoided in most patients with haemoglobin thresholds above 7 g/dL to 8 g/dL.

Continue reading

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Cartilage – the most stubborn entity of all?

Sad to see when new methods fail to improve outcomes. The image is CC by Karly Crystal
Sad to see when new methods fail to improve outcomes. The image is CC by Karly Crystal

I’ve previously [written](http://gforge.se/2012/07/cartilage-defects-part-iv/) about some interesting studies on treatment of cartilage defects. I was therefore thrilled to see Knutsen et al’s 15 year follow-up study. Unfortunately the results were rather disappointing; autologous chondorcyte implantation failed at a higher rate than microfractures, 40% vs 30%. Continue reading

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Setting up a multilabel classification network with torch-dataframe

Working with multiple outcomes per input can be challenging. The image is cc by  Markus Lütkemeyer.
Working with multiple outcomes per input can be challenging. The image is cc by Markus Lütkemeyer.

A common situation is that you have an image that can represent more than one class, e.g. a image may both have an oil tanker and an oil platform. You also may have missing data for some of these that you don’t want to evaluate. In my research this problem occurs and my solution so far has been a my own [criterion_ignore](https://github.com/gforge/criterion_ignore) that sets the errors for ignore labels to zero. This post will be a quick look at how to combine the torch-dataframe with the criterion_ignore. Continue reading

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Integration between torchnet and torch-dataframe – a closer look at the mnist example

It's all about the numbers and getting the tensors right. The image is cc by David Asch .
It’s all about the numbers and getting the tensors right. The image is cc by David Asch
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In previous posts we’ve looked into the basic structure of the torch-dataframe package. In this post we’ll go through the [mnist example][mnist ex] that shows how to best integrate the dataframe with [torchnet](https://github.com/torchnet/torchnet). Continue reading

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The torch-dataframe – subsetting and sampling

Subsetting and batching is like dealing cards - should be random unless you are doing a trick. The image is cc from Steven Depolo.
Subsetting and batching is like dealing cards – should be random unless you are doing a trick. The image is cc from Steven Depolo.

In my previous two posts I covered the most basic data manipulation that you may need. In this post I’ll try to give a quick introduction to some of the sampling methods that we can use in our machine learning projects. Continue reading

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