The category that eats the chart

sex on clinical-trial has 2 values, and one of them is 55.3% of the data.

• F — 498 rows, 55.3%
• M — 402 rows, 44.7%

The top three take 100.0% between them. With only 2 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select sex,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(baseline_score)::numeric, 2)             as avg_baseline_score
from clinical_trial
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_baseline_score tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.

Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.