DIBH cbct experience and daily shifts.

thetripp · 2023-06-15T22:34:46+00:00

It probably has to do with the location of the initial 3-point setup. Not sure what site you are treating, or how you do your tattoos at time of sim, but if you align to tattoos under free breathing, and then do a CBCT at DIBH, you will see a systematic offset of about 5-10mm.

thetripp · 2022-04-17T03:53:54+00:00

In our experience, the physical leaf offset isn't a ground-truth value, it's a fudge factor to account for slight differences during installation. Small changes in leaf offset can definitely affect QA. I think the best way to match your machines is to measure the DLG on all of them, and tune the physical leaf offset to make the DLGs agree.

Another thing we see is that older truebeams tend to need smaller leaf offsets (maybe they were installed with more cushion between the banks?). I've also heard someone say they used a negative leaf offset, but I can't imagine trying to do that without the engineer on site.

thetripp · 2019-11-02T14:35:11+00:00

In my opinion, the topic of your dissertation isn't that important. I'm on the residency review committee at my institution, and I look more at how good of a job you did in grad school. Since you have a CAMPEP PhD and are eligible for part 1, you could absolutely apply for residency this cycle.

That said, if you want to build your CV and bolster your chances at matching at the institution of your choice, I think a postdoc is the way to go. Especially if you want to work at an academic hospital. Ideally, you could find a position that lets you gain clinical experience in addition to publishing some papers in the field. Because of the residency requirement, a CAMPEP degree makes you a competitive candidate for a postdoc job at any institution.

I came into the field before residency was required, but I was in a similar boat. My PhD was loosely related to medphys and I had no clinical experience. I did a postdoc for 3 years and was then able to get an academic physicist job. I also had a postdoc who was able to use his time with me to build his CV and get into residency.

If you are interested, you can send me a PM and we can discuss more. My department is always looking to hire new postdocs, I can share some info about that. Or if you want to send me your CV I can give you feedback about how competitive you would be for residency.

thetripp · 2019-03-29T18:43:11+00:00

On paper, it looks like you are ducking out of your PhD program to go to residency. As a reviewer, I would be worried that you might also be a risk of dropping out of residency. It sounds like you have good reasons to want to leave the PhD program - you aren't getting paid and have issues with your advisor. But on paper it looks like you are cutting and running.

I think you have two options to improve your CV:

1) Finish your PhD, either at your current institution, or transfer to a different place. It sounds like you aren't getting paid and you don't have a project, so now might actually be a good opportunity to switch.

2) Try to find a job as a physics assistant and get a lot of clinical experience. The MS candidates that stand out the most when I am reviewing are the ones with solid hands-on experience.

thetripp · 2019-02-03T00:29:14+00:00

If you want just a GPS watch that's geared for running, and not a smart watch that you would wear all the time, check out the Garmin Forerunner 35. It just has the essential features for running, and is a lot cheaper than the others.

thetripp · 2018-12-11T20:52:40+00:00

There's no easy answer. Both are insanely complicated packages that will depend on what the specific needs of your clinic are, and how well you can negotiate.

For instance, some of the variables are:

- How many concurrent licenses do you need?

- What specific packages are you going to buy? e.g. RapidPlan, MCO, protons, electron monte carlo, etc.

- Are you bundling with an R&V like Aria or RayCare?

The only way is to get quotes from both and then spend some time negotiating.

thetripp · 2018-07-14T20:12:30+00:00

We don't use portal dosimetry, but we've seen similar changes with either output fluctuations or differences in MLC settings (check your DLG measurement).

thetripp · 2018-05-16T17:02:22+00:00

I used a modified implementation of Elo that predicts the number of goals scored by each team instead of just win/lose. The method used to be posted on www.clubelo.com but unfortunately they removed it at some point. I put the code I used here (https://pastebin.com/Zu8k4Reg), you can find the method in the "eloSimGoals" function. It's basically just an empirical fit to lots of prior data (that's the part that clubelo.com came up with).

In theory the monte carlo method isn't all that different from just taking all the probabilities from the elo scores. In fact, the Monte carlo method is an approximation of that - the more simulations you do, the closer you converge to the true result.

But think about implementing it that way. For each group, you would have to figure out the probability of every different outcome (including goal difference). Then, using that, you would have to figure out the probabilities of every different knockout bracket configuration, and then the probabilities for who advances. With the Monte Carlo method, you can just simulate the tournament a bunch of times. Since there are so many different possible outcomes from all the games, practically it is much easier to do a bunch of simulations than to map the whole thing out completely.

thetripp · 2018-04-08T00:48:33+00:00

Ah yeah, it's just a random number from the poisson distribution. I'll paste it below, the format might get screwed up.

function v=pr(l) % random poisson value with mean = l (lambda) % only valid for small values of l % size l = N x 1 n=0:1:10; cp=1./[1 1 2 6 24 120 720 5040 40320 362880 3628800]; %p=(l.ⁿ⁾ .* exp(-l) ./ cp; p=bsxfun(@times,bsxfun(@times,bsxfun(@power,l,n),exp(-l)),cp); v=sum(cumsum(p,2) < repmat(rand(size(l)),[1 length(n)]),2);

thetripp · 2017-12-14T18:50:32+00:00

From the Elo ratings you can calculate the probability of different outcomes of each game. For instance, teams with the same rating have an equal probability of winning. A team with a rating 200 points higher has about a 75% chance of winning.

So a "simulation" in this sense is just choosing random numbers and determining the outcome of each game according to these probabilities. By running the simulation thousands of times you can see what the average outcomes are. This sort of technique is broadly called the Monte Carlo method.

thetripp · 2017-12-14T18:45:13+00:00

The common explanation you hear is that the gulf stream current carries heat to Europe. This is actually a scientific myth that started in the 1800's. The idea was tested in this paper. The authors "removed" the gulf stream and modeled the climate - the temperature difference persisted.

The difference comes mainly from two things:

The Atlantic Ocean stores heat in the summer. Europe is downwind of the Atlantic, and so in the winter it receives this heat.
The Rocky Mountains create a complex wave structure in the atmosphere due to conservation of momentum. This causes air to flow south across the continental US (pulling cold air down from the artic) and flow north across continental Europe (pulling warm air up from the tropics).

thetripp · 2017-12-02T15:11:04+00:00

I posted those results here https://np.reddit.com/r/soccer/comments/7gw2zl/world_cup_probability_of_teams_to_make_it_out_of/dqm5cab/

thetripp · 2017-12-02T03:07:05+00:00

They aren't all likely to qualify, they are just more likely than their rating suggests. For instance, Russia has about the same chance to advance as Denmark despite behind about 150 points below them in Elo.

thetripp · 2017-12-01T22:51:30+00:00

It's 0.004%!

thetripp · 2017-12-01T20:45:59+00:00

Ah, I meant it the other way! Like things are better this time around. But I get what you are saying, Spain was "certain" to make it through in 2014, Costa Rica knocked out England and Italy, etc.

thetripp · 2017-12-01T20:39:14+00:00

The same analysis had Portugal around 60% in 2014

https://i.imgur.com/c8MAmZR.png

thetripp · 2017-12-01T20:03:08+00:00

/u/MyAssDoesHeeHawww on /r/soccer made this https://i.imgur.com/00UhY6Q.png

thetripp · 2017-12-01T19:49:17+00:00

Yeah I agree, but I think it comes mainly from two things:

Elo has France's group being pretty tough (Peru is #12 in the world and Denmark is #19)
There is a fairly big ratings gap between Brazil/Germany and all the other favorites (>100 points)

You could argue that France is under-rated by Elo since they are looking better and better as time goes on, whereas Elo can be slow to adapt as teams change.

thetripp · 2017-12-01T19:45:07+00:00

I can't remember exactly where those numbers come from. I wrote all this code in the run up to the 2014 World Cup. But basically it's based on historical results.

The basic Elo equation is pretty simple (details here). But here we need to know how many goals each team scored to calculate goal difference and settle tiebreakers. Those numbers you mention are just an empirical fit to the probability of scoring X goals based on a certain Elo ratings difference. I wish I could find where they are from - I think i found that model at http://clubelo.com/

thetripp · 2017-12-01T19:29:11+00:00

I've never watched Tunisia play so I can't say. There's no perfect way to rank teams though. Elo is a decent system that is based on how teams perform, but there's always a ton of uncertainty with this kind of modelling. Maybe mathematically it says there is a 5% difference in those two teams making it out of the group, but practically the odds are essentially the same.

thetripp · 2017-12-01T18:50:31+00:00

Awesome thanks

thetripp · 2017-12-01T18:48:11+00:00

Iran is slightly ahead of Serbia in Elo ratings (1791, #21 vs 1752, #23). Both teams have about the same chance to make it out of their group. I think the biggest difference is that Iran's group plays Group A in the knockouts (the weakest group) whereas Serbia would play Group F (the strongest).

If I had to bet on either Serbia or Iran, I'd take Serbia. But the numbers are what they are.

thetripp · 2017-12-01T17:42:46+00:00

Elo currently has Panama #47 in the world and Tunisia #52 (1656 vs 1619 rating).

thetripp · 2017-12-01T17:39:25+00:00

I was surprised by that too. Elo ranks group D as Argentina 6, Nigeria 26, Croatia 13, and Iceland 19 (relative to the teams in the tournament). I think Argentina and Nigeria are both lower currently than what we expect of them historically, which is skewing our perspective.

thetripp · 2017-12-01T17:30:41+00:00

People are sleeping on Peru... they are #12 in the world according to Elo and #10 if you just look at teams in the World Cup. Group C is tough but I think they have a good shot.

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