An Egocentric Life-Saving Interventional Procedure Dataset of Actions, Medical Questions, Maneuvers and Tools
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
This paper introduces the Trauma THOMPSON dataset, designed to advance AI-driven decision support for life-saving interventions (LSIs) in emergency care, particularly in resource constrained humanitarian settings. The dataset comprises 3,717 high resolution and egocentric video clips of both regular and just-in-time (JIT) procedures. The JIT procedures consists of videos of the same LSI procedures, but with makeshift tools, and is useful for studying human medical commonsense. Each clip is annotated by medical professionals with verb-noun format, such as "take scalpel" and "make incision". In addition to action segments, the dataset includes annotations for medical visual question answering (MVQA), hand maneuvers, and object detection. Eventually, these rich annotations and dataset can be used to train an AI agent to advise first-responders in the field about what to do next with the resources at hand. We provide benchmarks for action recognition, anticipation, and MVQA using state-of-the-art machine learning models.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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