Surgical data recording in the operating room: a systematic review of modalities and metrics
Bibliographic record
Abstract
INTRODUCTION: Operating room recording, via video, audio and sensor-based recordings, is increasingly common. Yet, surgical data science is a new field without clear guidelines. The purpose of this study is to examine existing published studies of surgical recording modalities to determine which are available for use in the operating room, as a first step towards developing unified standards for this field. METHODS: Medline, EMBASE, CENTRAL and PubMed databases were systematically searched for articles describing modalities of data collection in the operating room. Search terms included 'video-audio media', 'bio-sensing techniques', 'sound', 'movement', 'operating rooms' and others. Title, abstract and full-text screening were completed to identify relevant articles. Descriptive statistical analysis was performed for included studies. RESULTS: From 3756 citations, 91 studies met inclusion criteria. These studies described 10 unique data-collection modalities for 17 different purposes in the operating room. Data modalities included video, audio, kinematic and eye-tracking among others. Data-collection purposes described included surgical trainee assessment, surgical error, surgical team communication and operating room efficiency. CONCLUSION: Effective data collection and utilization in the operating room are imperative for the provision of superior surgical care. The future operating room landscape undoubtedly includes multiple modalities of data collection for a plethora of purposes. This review acts as a foundation for employing operating room data in a way that leads to meaningful benefit for patient care.
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How this classification was reachedexpand
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".