| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| tags: |
| - medical |
| - robustness |
| - benchmark |
| - arxiv:2610.08585 |
| pretty_name: MedDistractNotes |
| configs: |
| - config_name: pairs |
| data_files: |
| - split: test |
| path: data/ambient/pairs_v3.parquet |
| default: true |
| - config_name: insertions |
| data_files: |
| - split: test |
| path: data/ambient/insertions_v3.parquet |
| - config_name: organ_systems |
| data_files: |
| - split: test |
| path: data/ambient/encounter_systems.parquet |
| - config_name: notes |
| data_files: |
| - split: test |
| path: results/raw_v3/amb_notes.parquet |
| - config_name: judgments |
| data_files: |
| - split: test |
| path: results/raw_v3/amb_judgments.parquet |
| - config_name: attribution |
| data_files: |
| - split: test |
| path: results/raw_v3/amb_attribution.parquet |
| - config_name: single_note_judgments |
| data_files: |
| - split: test |
| path: results/judge_control/judgments.parquet |
| - config_name: failure_modes |
| data_files: |
| - split: test |
| path: results/v3/amb_failure_notes.parquet |
| --- |
| |
| # MedDistractNotes |
|
|
| MedDistractNotes tests whether incidental conversation enters clinical notes. It contains **1,152 clean–distracted transcript pairs from 576 encounters**, with one bystander and one nonliteral aside per encounter. The clean and distracted transcripts differ only by the inserted exchange. Use the paired inputs to evaluate your own note-generation model; the additional configurations contain the frozen study outputs. |
|
|
| ```python |
| from datasets import load_dataset |
| pairs = load_dataset("NYU-OLAB/MedDistractNotes", "pairs", split="test") |
| row = pairs[0] |
| print(row["clean_transcript"], row["distracted_transcript"]) |
| ``` |
|
|
| The `test` split is the entire evaluation benchmark. `source_dataset` preserves the original corpus split; these are not new training/development partitions. The same encounter appears in both perturbation families, so split by encounter if making your own partitions. |
|
|
| ## Contents and keys |
|
|
| | Configuration | Rows | Contents | |
| |---|---:|---| |
| | pairs | 1,152 | Clean/distracted transcripts, reference note, inserted exchange, target summary and insertion position | |
| | insertions | 1,152 | Frozen GPT-generated exchanges, assigned content, organ system and generation metadata | |
| | organ_systems | 577 | Labels/rationales for the original cohort, including the one excluded single-speaker encounter | |
| | notes | 18,432 | Saved clean/distracted notes from eight models | |
| | judgments | 18,432 | Paired contamination/severity judgments and note-quality scores | |
| | attribution | 3,921 | Attribution and clinical-use classifications for flagged notes | |
| | single_note_judgments | 18,432 | Matched single-note control scores and explanations | |
| | failure_modes | 9,216 | Frozen outcome, failure-mode and section-location annotations for distracted notes | |
|
|
| Pair keys are `source_dataset`, `item_id`, `distractor_type`. Add `model` and `condition` to join per-note results. `model` retains the identifiers used in the frozen runs. Judge explanations are model-generated, not clinical adjudications. Two empty saved note records remain in the primary denominator; absent and unresolved attribution classifications are retained. Missing scores must not be treated as zero. |
|
|
| `protocol/prompts.json` contains the generation and scoring templates. Source data, outputs and scores are unchanged from the checked submission archive; only provider bookkeeping was removed from the single-note control export. `release_manifest.json` records content hashes and `data_schema.json` describes every column. The toolkit supports downloading these files into the expected analysis paths. |
|
|
| ## Sources and license |
|
|
| Derived from [ACI-Bench](https://github.com/wyim/aci-bench) (Yim et al., 2023) and [MTS-Dialog](https://github.com/abachaa/MTS-Dialog) (Ben Abacha et al., 2023), both under CC BY 4.0. The released cohort uses 77 ACI-Bench encounters and 499 MTS-Dialog excerpts. The original transcripts and reference notes are retained; the changes are generated incidental exchanges and the associated model outputs/annotations. Cite the source corpora as well as this benchmark. This release uses CC BY 4.0 with source attribution retained. |
|
|
| ## Intended use and limitations |
|
|
| For research on contamination, attribution and robustness in note generation. The asides are synthetic and the reference notes come from the source corpora. The scores are automated judgments, not clinician-validated safety labels. High general note-quality scores do not establish that a note is free of contamination. Do not use the benchmark as patient-care guidance. |
|
|
| Code: https://github.com/nyuolab/llm_distract (the public toolkit release is being prepared). |
| |
| ## Citation |
| |
| [Paper](https://arxiv.org/abs/2610.08585). The citation below uses the publicly posted v1 title; the authors have submitted a revised title. The scientific data are frozen to the submitted study. |
| |
| ```bibtex |
| @misc{vishwanath2026incidental, |
| title={Incidental information contaminates patient notes and disrupts clinical reasoning in large language models}, |
| author={Vishwanath, Krithik and Ye, Brandon and Alyakin, Anton and Markert, John E. and Hsieh, Aaron and Mańkowski, Michał and Oermann, Eric K.}, |
| year={2026}, eprint={2610.08585}, archivePrefix={arXiv}, primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2610.08585} |
| } |
| ``` |
| |
