Here is a graphic of changes in muscle and strength in transwomen pre- and post- testosterone suppression (12+ months), compared with baseline metrics from demographically matched females.
The original data is presented in Hilton and Lundberg, 2021 (Table 4).
The graphic was created by me for a policy paper I coauthored with Professor Jon Pike @runthinkwrite and Professor Leslie Howe @usask for the Canadian think tank The MacDonald Laurier Institute.
A consistent theme in response to this data has been to argue that even if transwomen don’t lose very much muscle or strength, it may sufficient to put them within ‘female range’.
Here is a graphic showing the muscle and strength changes pre- (anchored to nominal 100%) and post- testosterone suppression, mapped not just against females (the data already published) but also against control males.
Statistical analysis across the dataset shows there is no significant difference in muscle and strength metrics in transwomen pre- and post- testosterone suppression.
Furthermore, pre-testosterone suppression, transwomen are 7% smaller/weaker than control males and 35% bigger/stronger than females.
Post-testosterone suppression, they are 11% smaller/weaker than control males and 30% bigger/stronger than females.
The argument was: Ok, transwomen may not lose very much, and apparently remain some large percentage ahead of females, but if both TW and females are really different to (control) males at baseline, it’s really just jostling for position in the ‘female range’.
I don’t believe that argument is evidenced in the data. Arguably, we could have run that analysis in our original review. Maybe we didn’t realise that people would start to say that TW were measuring more like female from baseline.
Mea culpa.
It was clear when Tommy and I reviewed the data that transwomen in some (not all) studies were, at baseline, smaller/weaker than control males. This has been, in that literature, attributed to low exercise uptake, comorbid eating disorders etc. I’ve always stated that as a known.
But it’s helpful to see the magnitude of differences and changes in a chart. With some stats.
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I recently tweeted about people who think I believe humans are asparagus.
This bad faith take stems (ha ha) from an analogy I’ve used to illustrate that the phenomenon of male/female is not limited to the constructions of the human brain.
Like many plants, and like humans, (some) asparagus strains are dioecious - they exist as individuals male and individual female plants. In animals, we call this set up ‘gonochorism’.
Asparagus can reproduce via the fusion of one small and one large gamete (sometimes, they reproduce asexually).
Biological convention denotes the plant morph producing the large gamete, found in the ovules, as ‘female’.
Systematic differences between the two sexes of a gonochoristic species of a physical characteristic (or set thereof), not including reproductive anatomy.
Some sexually dimorphic characteristics are non-overlapping (e.g. deer antlers) while some are very overlapping (e.g. human height).
The extent of overlapping observation/measurement is irrelevant. The only requirement is a robustly-detectable difference between sexes.
Many female humans are taller than many male humans, yet the population descriptions of height in humans consistently reveal that males as a sex class are taller than their demographically-matched female peers.
Height in humans is a sexually dimorphic characteristic.
@xandvt@MumpGorithm@refined_devon@BBCMorningLive@BBCiPlayer I am honestly appalled by your behaviour here. You are a medic and a public communicator, and you seem unable to use basic and commonly-understood words when discussing concepts like population health screens.
@xandvt@MumpGorithm@refined_devon@BBCMorningLive@BBCiPlayer The WHO make it clear that an ethical population screen uses clear language that will maximise capture of the target demographic. Who are the target demographic for prostate screens?
@xandvt@MumpGorithm@refined_devon@BBCMorningLive@BBCiPlayer To define the population demographic for prostate screening as ‘those with prostates’ lacks any explanatory value. It’s a linguistic dead end. Replace ‘prostate’ with a less well-known structure, and then consider how effective a screening campaign will be….
First, there is the stuff about how to classify CAIS and there is discussion within this thread about the dev bio, endocrinonlogy etc.
@WackyPidgeon@JamesVSD1@zaelefty@JuliaMasonMD1@madadhruadh@hoovlet I have never hidden my developmental biology understanding of sex, which is centred not on chromosomes (or any other determination mechanism) but on gamete type, which is in animals a product of gonad type.
@tomhfh Tom. Promise me you will never teach statistics.
The graph you have posted clearly shows two overlapping normal distributions.
Each normal distribution is associated with either the female sex or the male sex.
@tomhfh As you correctly point out, short males are not female.
Yet a very short male may appear in the little area of overlap highlighted, because they are at the far left of the male normal distribution, not because they are magically ‘intersex’ or ‘a bit female’.
@tomhfh The X axis in the graph is not ‘sex units’. The graph is not mapping sex. It is a mapping schematically a characteristic associated with sex, like testosterone levels (in some concentration unit).
Sex is why you have a bimodal distribution of testosterone levels.