This is a phase 1 trial (usually just undertaken to check safety before larger trials are done to test efficacy and dose), so it seems all the more amazing that such a big improvement was seen.
Think about that for a second. Nearly blind, or actually blind, to being able to read again.
I'm seriously debating specializing in ophthalmology, and sub-specializing in retina surgery just became an even more appealing career plan for me.
Depends if you like dealing with people, right?
I had a retinal tear 4 weeks ago and then then retina detached and started sagging in front of my lens, blocking half of my field of view. Seriously scary, especially since my mother has lost most of her vision after a similar problem.
I got a pneumatic retinopexy a couple of days later. Amazingly, it only took about 30 minutes in a (nice) back-room of the retina clinic with local anesthesia, and the retina re-attached itself in the course of the next few days. Now I am just waiting for the gas bubble to dissolve while my vision is getting better each day.
I don't have much eyesight left, but a retinal detachment surgery helped make sure I did have some.
It's a worthy endeavor, trust me.
In a very sincere and unironic way, "my faith in humanity is restored".
Phase 1 trials typically get little publicity unless they either kill someone or turn in a statistically-significant improvement. (A phase 1 trial in which the controls do exactly as well as the experimentals and no side-effects are observed, which is highly likely for even excellent treatments because of the tiny sample size, will very rarely be written up because it's boring.) And because their sample sizes are always tiny, any statistically-significant improvement will always be an extremely large effect size. If the effect hadn't been improbably large, you would almost certainly not be reading about it now on HN.
And because of this selection bias, the effects you hear about tend to be massively overestimated. This is one of Andrew Gelman's points: the 'statistical significance filter' massively inflates effect sizes, a type M error, which then subsequently regress to the mean of the true smaller effect. This is why you're not supposed to take Phase I trials as meaningful estimates of the effect size, why statisticians emphasize they're supposed to be about safety, and why you don't do power analysis based on the observed results (either post hoc or for designing the next big ones). This is also part of why you hear about so many amazing pilot experiments in animals or humans but then the big followup trials are much more modest or nulls.