The Trauma THOMPSON Dataset for Real-World Emergency AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present the Trauma THOMPSON dataset and benchmarks designed to advance artificial intelligence research for real-time decision support in emergency and austere medical environments. The dataset contains 220 unscripted egocentric videos of five emergency procedures, including a diverse collection of "just-in-time" (JIT) life-saving interventions performed under resource-constrained conditions. These JIT scenarios more closely reflect the realities of humanitarian and field-based operational medicine, where standard protocols must often be adapted or creatively executed. To support deeper visual understanding, we introduce two new layers of fine-grained annotations: object detection labels for critical medical instruments and supplies and hand annotations to facilitate hand tracking and surgical skill assessment. These additions enable new research directions in spatiotemporal reasoning, interaction modeling, and AI copilots that interpret and guide complex procedures in real time. The Trauma THOMPSON dataset includes benchmark tasks in action recognition, action anticipation, visual question answering (VQA), object detection, and hand localization. We evaluate state-of-the-art models across these tasks, identifying current strengths and open challenges in developing robust AI for field-deployable decision-making. The dataset is available at <a href='https://github.com/zhuoyp/TTD'>https://github.com/zhuoyp/TTD</a>, and it can serve as a foundation for building intelligent systems that assist frontline caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it