def _aggregate_get_clipped_mean(
    single_eval_results: list[SingleEvalResult],
) -> EvalResult:
    """
    Aggregate multiple SingleEvalResults into a single EvalResult for HealthBench.
    For each metric, returns the stats in _compute_clipped_stats.
    """
    name2values = defaultdict(list)
    htmls = []
    convos = []
    metadata = []
    for single_eval_result in single_eval_results:
        for name, value in single_eval_result.metrics.items():
            name2values[name].append(value)
        if single_eval_result.score is not None:
            name2values["score"].append(single_eval_result.score)
        htmls.append(single_eval_result.html)
        convos.append(single_eval_result.convo)
        metadata.append(single_eval_result.example_level_metadata)
    final_metrics = {}
    for name, values in name2values.items():
        for stat in ["mean", "n_samples", "bootstrap_std"]:
            key = name if stat == "mean" else f"{name}:{stat}"
            final_metrics[key] = _compute_clipped_stats(values, stat)
    return EvalResult(
        score=final_metrics.pop("score", None),
        metrics=final_metrics,
        htmls=htmls,
        convos=convos,
        metadata={"example_level_metadata": metadata},
    )

    Aggregate multiple SingleEvalResults into a single EvalResult for HealthBench.
    For each metric, returns the stats in _compute_clipped_stats.
    
healthbench_eval.py