Aggregated purity results across four services, with caveats — a second dataset

Aggregated purity results across four services, with caveats — a second dataset — setting out what I have, and where I think it stops being reliable.

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.

Collection methods: ask how the data were collected. Longitudinal tracking over months is stronger than retrospective recall. Prospective measurement (done while experiencing something) is stronger than memory afterward.

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.

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.

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.

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.

Combining data from different sources: datasets from this site are not directly comparable to published trials because the populations are different. They are worth reading separately, not merged together.

post #7 answers the question as asked. The question underneath it is different.

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.

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).

This follows post #7 rather than contradicting it.

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

This follows post #8 rather than contradicting it.

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

I read post #10 twice before replying, because I had assumed the opposite.

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

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.

Combining data from different sources: datasets from this site are not directly comparable to published trials because the populations are different. They are worth reading separately, not merged together.