Augmented reality and human factors regarding the neurosurgical operating room workflow
Bibliographic record
Abstract
Augmented reality (AR) continues to be heavily studied as a research topic for potential medical use. The goal of seeing the patient’s anatomy below the surface of the human body has always been thought of as the ideal surgical navigation tool. Rather than observing medical imaging, such as computed tomography (CT) or magnetic Resonance (MR) images on a monitor, hospital personnel would be able to see patient specific pathologies through Augmented Reality (AR) glasses. Neurosurgery has commonly been a field of choice for AR integration because of the many needs that can potentially be met. Understanding AR in the neurosurgical Operating Room (OR) does pose some benefits well as concern in terms of human computer interaction (HCI). One of the core concepts of HCI is the idea of user-centered design. While one aims to create an intuitive interface for the user-group, introducing AR into the OR can increase cognitive overload and inattentional blindness if executed improperly without considering the full use-case and all stakeholders. A common application of neuro-navigation is in spinal surgery, which, while incredibly accurate, disrupts OR workflow. These devices drastically improve patient outcomes yet are seldom employed because of these disruptions. HCI concepts can better integrate AR into the OR to solve pitfalls observed in modern neuro-navigation, and gives designers, engineers and surgeons the necessary tools to develop AR solutions. Our goal is to thoroughly analyze the OR workflow such that AR can be effectively incorporated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".