A calculation on public biomedical data can produce a difference that seems worth explaining. Before expanding it into a larger analysis, I needed to check whether someone had already reported the phenomenon and whether the data could answer the question I was asking. None of the leads in this 2026 search became a verified new discovery.
I started from public data and reported limitations in 2025–2026 research, then compared leads with nearby prior work, public issue records and registries. Each lead needed a small test that could stop it before a much larger analysis began. A kill test specified what would make me abandon that direction; it was not a score for selecting the most attractive result.
Letting alternative explanations through first
An unusual value does not establish a new phenomenon. If a public record had already documented the issue, reproducing it in my calculation did not make it an independent new discovery. Changing the search terms or renaming an indicator would not resolve that overlap.
I also checked whether the observations could distinguish the proposed explanations. If two different explanations can generate the same observations, choosing one and calling it the cause does not become justified by doing more computation. This is the problem of identifiability. When the necessary variables or comparison conditions were missing, downloadable data alone did not justify continuing.
Some leads reached actual code and pilot calculations. I examined data joins and missing observations, and tested whether those conditions could produce similar patterns. These small checks helped separate a problem that more computation might address from one that required a different question or different data.
Returning to the reason for a stop
The search ledger preserved on 1 August 2026 had no lead promoted to the next stage. For each lead, the record retained nearby prior work, data requirements, a falsifying test and the reason for stopping. That gives me somewhere to return when a familiar idea appears under a different title.
The decisions applied to the available data and the conditions I had set. They were not permanent judgments that a topic was worthless. New data, or a way to distinguish explanations that previously looked identical, could justify another look. The earlier failure would still need to be addressed.
Candidate names, identifiers and unpublished measurements remain private. The retained code, summary outputs and audit tables document the stops, but the archive does not contain every historical web response or every large source dataset. Repeating a calculation would begin with its original sources and the archive’s exclusions. Before generating another set of leads, I would first check whether the condition that stopped an earlier one had actually changed.