ThreadMed-QA contains multi-turn medical question-and-answer threads collected
from the public r/AskDocs subreddit. Although all Reddit usernames, post/comment
identifiers, and timestamps have been removed and replaced with anonymized
THREAD_XXX identifiers, the underlying free text can still contain sensitive
personal health information. By requesting access you agree to the following:

Research use only. You will use the dataset solely for non-commercial
academic research or education.
No re-identification. You will not attempt to link records back to any
individual, account, or original Reddit post, including via search engines
or scraping.
No clinical use. You will not use this dataset (or models trained on it)
to provide, augment, or automate clinical decision-making, diagnosis, or
treatment for real patients.
No redistribution. You will not redistribute the raw data. You may share
derived artifacts (e.g. model outputs, aggregate statistics, filtered
subsets substantially transformed for a downstream study) under the same
terms.
Ethics approval. You confirm that your intended use has been reviewed
by your institution's IRB / ethics board or is exempt under its policies.
Removal on request. If a specific thread is flagged for removal by the
original poster, the maintainers may remove it from future releases; you
will delete any local copies of removed threads within 30 days of being
notified.

Requests are reviewed manually; please allow up to 7 days.

ThreadMed-QA

Multi-turn medical question-answering threads from r/AskDocs, anonymized for research release.

ThreadMed-QA preserves the original dialogue flow between a patient-asker and one or more verified medical professionals on r/AskDocs, rather than collapsing each thread to a single question-answer pair. The dataset was constructed to support research on multi-turn patient-facing question answering, clinical dialogue modeling, and safety evaluation of LLM medical assistants.

Language: English
License: CC BY-NC 4.0 (with additional DUA — see above)
Access: Gated. Please read the Data Use Agreement above and request access.
Point of contact: @monicamunnangi23 on the Hugging Face Hub, or via the Community tab of this repository.

Configurations

The repository provides two configurations that share a single THREAD_XXX identifier namespace, so the same thread can be cross-referenced across configurations.

Config	Threads	Description
`raw`	9,741	Full multi-turn corpus. Threads may end with an unanswered follow-up.
`fully_answered`	2,437	Analysis-ready subset in which every patient turn is followed by a verified-professional response. Strict subset of `raw`.

Load either config with the datasets library:

from datasets import load_dataset

raw = load_dataset("<HF_USERNAME>/threadmed-qa", "raw")
fa  = load_dataset("<HF_USERNAME>/threadmed-qa", "fully_answered")

Data fields

Each line of the JSONL is a single thread:

Field	Type	Description
`thread_id`	`str`	Anonymized identifier, e.g. `THREAD_001` … `THREAD_9741`. Stable across configurations.
`has_medical_professional`	`bool`	Whether at least one reply in the thread comes from a verified medical professional.
`source`	`str`	Collection provenance tag from the upstream construction pipeline.
`qa_pairs`	`list[Turn]`	Ordered dialogue turns from opening post to final reply.

Each turn in qa_pairs:

Field	Type	Description
`q`	`str \| null`	The patient's question / follow-up text. `null` only in edge cases where a turn contains only a professional reply.
`a`	`str \| null`	A response to that turn. `null` if no reply exists (common at the end of an unresolved thread).
`a_author_flair`	`str \| null`	Reddit user flair indicating the responder's professional role, e.g. `"Physician"`, `"Registered Nurse"`. Only reliable when `has_medical_professional=true`.

Example (`THREAD_001`)

{
  "thread_id": "THREAD_001",
  "has_medical_professional": true,
  "source": "all",
  "qa_pairs": [
    {"q": "Test where doctor ran his fingers from my feet up my legs? ...", "a": null, "a_author_flair": null},
    {"q": "I don't think he did... which is what made me wonder. ...",       "a": "Common test for peripheral neuropathy ...", "a_author_flair": "Physician"},
    {"q": "Thank you both for taking time to respond and for clearing that up!", "a": null, "a_author_flair": null}
  ]
}

Dataset creation

Source

Public posts and comments from the r/AskDocs subreddit. r/AskDocs is a community in which users post health-related questions and verified medical professionals (identity confirmed by community moderators via credential review) respond. Verified professionals are marked in Reddit by a flair tag such as Physician, Registered Nurse, Physician Assistant, etc.

Collection window

All data was collected before June 2023, prior to the Reddit API access policy changes, under the API terms in effect at the time of collection. Raw data comes from public Reddit dumps and adheres to the same collection conventions as prior academic corpora such as MedRedQA (Nguyen et al., 2023).

Curation steps (summary)

Filter posts to the r/AskDocs subreddit.
Reconstruct the reply tree per submission.
Alternate patient / responder turns to produce a qa_pairs sequence.
Retain only threads where at least one reply comes from a verified medical professional (has_medical_professional = true).
Trim and normalize whitespace; drop links, image-only posts, and moderator/bot boilerplate.
Produce the fully_answered subset by requiring that every patient turn is followed by a verified-professional reply.

For the full pipeline, see the accompanying paper.

Anonymization

The following were removed prior to release:

All Reddit usernames (from the post_author field and any inline u/… mention in question or answer text — mentions were replaced with the token [USER]).
All Reddit post / comment identifiers (link_id, id, etc.).
All exact timestamps (created_utc).
All URLs (http://…, https://…, www.…) — replaced with [URL] if any had survived upstream link stripping.

Each thread was assigned a stable anonymized identifier of the form THREAD_XXX, where the numeric portion reflects position in the sorted raw corpus (with 3-digit zero padding, expanding for 1000+). The mapping from original Reddit identifiers to THREAD_XXX ids is retained privately by the maintainers and is not part of this release.

Free-text content in the questions and answers is preserved verbatim except for the substitutions above.

Considerations for using the data

Personal and sensitive information

Even after structural anonymization, medical free text in the questions and answers may contain sensitive personal health information (symptoms, medications, dates, locations, family history). Users of this dataset must treat it accordingly:

Do not attempt to search verbatim quotes back to the original Reddit post.
Do not publish long verbatim excerpts of the source text; paraphrase when possible, especially in released examples.
Do not attempt to re-link ids across releases, papers, or leaked datasets.

Not a clinical resource

Responses come from Reddit users who self-identified as medical professionals and were then verified by community moderators. They are not substitutes for professional medical advice, and this dataset is not a source of clinical ground truth. Models trained on ThreadMed-QA must not be deployed for real clinical decision-making.

Known limitations and biases

Population bias. English-speaking Reddit users, skewed toward demographics active on the platform (US-heavy, likely younger).
Answer bias. Retained responses are those that were up-voted on Reddit by the AskDocs community, not necessarily the medically optimal answer.
Coverage bias. Common outpatient / dermatological / mental-health concerns are over-represented relative to inpatient and acute care.
Verification bias. Professional flairs are moderator-verified but not perfect; occasional impostor content may survive filtering.
Temporal bias. All data pre-dates June 2023; medical guidance evolves.

Related datasets

MedRedQA (Nguyen et al., IJCNLP 2023 — 10.25919/YN7X-9148): 51k single question–answer pairs from r/AskDocs. ThreadMed-QA is complementary: instead of the highest-scoring physician reply as a single answer, ThreadMed-QA preserves the full multi-turn interaction, which is required for research on conversational medical QA, clarification-question modeling, and safety-through-time.

Licensing

Dataset content is released under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), with the additional Data Use Agreement above.

Takedown / removal requests

If you are the original poster of a thread in this dataset and would like it removed, please open a private request via the Community tab of this repository, or contact @monicamunnangi23 on Hugging Face. Please include the THREAD_XXX identifier (or, if you can identify it, a short verbatim excerpt) and the maintainers will remove it from subsequent releases.

Citation

The full citation for ThreadMed-QA will be added here once the accompanying paper is publicly available. Please check back — or contact @monicamunnangi23 via the Community tab — before citing.

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