Coming back to: A community side-effect dataset, with its response rate and biases

Posting this under the heading it deserves: A community side-effect dataset, with its response rate and biases Everything below is what sits behind that.

Working through the identity arithmetic and I would like it checked.

retatrutide has a monoisotopic mass close to 4731.3 Da. On an electrospray instrument I would expect to see the multiply charged series rather than the intact singly charged ion, so for the doubly charged species I calculate (4731.3 + 2 x 1.00728) / 2, and for the triply charged the analogous expression.

The observed values in the report sit within a few ppm of those. My question is what that actually establishes, because I have seen people treat a mass match as a purity result and I do not think it is one.

Worth separating two things that the opening post runs together.

Limitations of datasets: all community-collected data has limitations. The population is self-selected (people in this community are not representative of all people using these compounds). Reporting bias is real (remarkable outcomes get reported; mundane outcomes do not).

Using data in discussions: datasets are useful as reference points when someone claims something unusual. "I have not seen that reported in the data" is different from "that is impossible", but data gives you something to say.

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

post #4 answers the question as asked. The question underneath it is different.

Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.

How to contribute: if you have longitudinal data you want to add, the format is simple: date, measurement, context. Contact the maintainer of the specific dataset.

Bias toward positive outcomes: datasets collected by members are biased toward people who found the compounds useful. People who did not respond do not return. People who had bad outcomes might have left the community.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.

Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.

Community-collected datasets: some members have compiled datasets from their own experience and shared them. They are self-reported, unblinded, and therefore limited as evidence. But they show real patterns that people experience.

Worth separating two things that post #9 runs together.

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

post #13 is right about the mechanism and I think understates the practical bit.

Bias toward positive outcomes: datasets collected by members are biased toward people who found the compounds useful. People who did not respond do not return. People who had bad outcomes might have left the community.