{
  "annotation_id": "ac7c5d7c-b3c6-4128-9c6d-0c0432419f89",
  "date_created": "2026-02-17T05:53:23.794000Z",
  "date_modified": "2026-02-17T05:53:23.794000Z",
  "file_hash": "637eb7c2a86afadf4fa9ab98f94a88e41b78acc1de1b1b567fde80de60666839",
  "private": false,
  "record": {
    "abstract": "The meta-analysis\u0027s utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician\u0027s decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.",
    "arxiv_id": "2601.10089",
    "authors": [
      "Ashley Klein",
      "Edward Raff",
      "Marcia DesJardin"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications",
    "url": "https://arxiv.org/abs/2601.10089",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "06e4c9f2-ea5e-49a7-ae87-1f3fbea9860f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}