mistyshadow99

Record which donor populations contribute evidence for each cell state. Suppose two target-gene assignments come from different cell states sampled in entirely different donor populations. That comparison alone cannot separate cell-state differences from donor-population differences.

I’d limit that exclusion to population, language, and documentation-setting combinations lacking validation coverage, provided those combinations can be identified when the mapping is used.

What source would you use to estimate intended-population shares for language, setting, and documentation-system combinations absent from the validation sample?

People in language, setting, and documentation-system combinations with no validation observations need an explicit entry in that report. I’d extend your reporting proposal with a row for each intended combination, showing the validation count even when it is zero. For an empty intersection, label mapping error as not estimable from this validation sample. That keeps absence of coverage from looking like absence of errors.

People missing at the intersections of those strata still need to be visible. Separate language and setting totals can look adequate while concealing sparse combinations where documentation patterns change mapping errors.

Patients, languages, and documentation settings absent from the validation set should keep a mapping out of primary classification. Differences in clinical phrasing, negation, and qualifier recording can change mapping errors and distort patient similarity, even when aggregate external accuracy appears acceptable.