README.md

---
license: apache-2.0
language:
- en
tags:
- medical
- biomedicine
- clinical-trials
- large-language-models
- supervised-finetuning
- evidence-based-medicine
- trial-design
- systematic-review

config_names:
- study_search
- study_screening
- evidence_summarization
- trial_design
- sample_size_estimation
- trial_completion_assessment

configs:
- config_name: study_search
  data_files:
  - split: train
    path: sft_study_search_data_cleaned.parquet

- config_name: study_screening
  data_files:
  - split: train
    path: sft_study_screening_data.parquet

- config_name: evidence_summarization
  data_files:
  - split: train
    path: sft_evidence_summarization_data.parquet

- config_name: trial_design
  data_files:
  - split: train
    path: sft_design_data.parquet

- config_name: sample_size_estimation
  data_files:
  - split: train
    path: sft_sample_size_data.parquet

- config_name: trial_completion_assessment
  data_files:
  - split: train
    path: sft_trial_completion_assessment_data.parquet
---
# TrialPanorama: Supervised Fine-Tuning Data for Clinical Research LLMs

## Dataset Summary

**TrialPanorama SFT** is a large-scale, task-oriented **supervised fine-tuning (SFT) dataset** designed to train large language models for **end-to-end clinical research and trial development workflows**.

The dataset is derived from **TrialPanorama**, a structured clinical research resource aggregating **1.6M+ clinical trial records** across global registries and linking them with biomedical ontologies and supporting literature. It focuses on transforming raw clinical trial data and curated evidence into **instruction–response pairs** that reflect realistic, expert-level research tasks.

The dataset supports training LLMs to operate as **clinical research assistants** capable of systematic literature review, trial design reasoning, and evidence-based decision making.

---

## Supported Training Tasks

Each task is released as a separate dataset configuration.

| Task (config) | Description |
|---|---|
| `study_search` | Given a clinical research question, retrieve and justify relevant studies from large trial and literature corpora. |
| `study_screening` | Perform inclusion/exclusion decisions for candidate studies based on eligibility criteria and study metadata. |
| `evidence_summarization` | Synthesize structured and unstructured trial evidence into concise, faithful summaries. |
| `trial_design` | Generate or refine clinical trial designs, including arms, interventions, and eligibility criteria. |
| `sample_size_estimation` | Estimate appropriate sample sizes under specified statistical and design assumptions. |
| `trial_completion_assessment` | Assess trial completion likelihood and rationalize risks using trial design and historical evidence. |

---

## Data Characteristics

- **Instruction-following format** suitable for SFT
- Grounded in **real clinical trial records**
- Emphasizes **clinical reasoning**, not surface text generation
- Covers both **systematic review** and **trial design & optimization** tasks
- Designed to support **generalist and agentic LLM training**

All files are provided in **Apache Parquet** format.

---

## Typical Fields

Each record may include:
- Task-specific **instruction or prompt**
- Structured **context** (trial metadata, eligibility criteria, outcomes, phase)
- **Model response targets** written or validated by domain experts
- Task and difficulty metadata

Exact schemas vary by task.

---

## Intended Use

This dataset is intended for:
- **Supervised fine-tuning of LLMs** for clinical research tasks
- Training **research-oriented AI agents** for trial planning and evidence synthesis
- Building domain-adapted models for **systematic review automation**
- Academic benchmarking of clinical reasoning capabilities

### Not Intended For

- Model evaluation (see TrialPanorama benchmarks for evaluation)
- Clinical decision making
- Direct medical or regulatory use

---

## How to Load

Load a specific training task via its configuration name:

```python
from datasets import load_dataset

ds = load_dataset("TrialPanorama/dataset", "study_screening")
ds["train"]