A Review of Cognitive Support Systems in the Operating Room
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
BACKGROUND: In recent years, numerous innovative yet challenging surgeries, such as minimally invasive procedures, have introduced an overwhelming amount of new technologies, increasing the cognitive load for surgeons and potentially diluting their attention. Cognitive support technologies (CSTs) have been in development to reduce surgeons' cognitive load and minimize errors. Despite its huge demands, it still lacks a systematic review. METHODS: Literature was searched up until May 21st, 2021. Pubmed, Web of Science, and IEEExplore. Studies that aimed at reducing the cognitive load of surgeons were included. Additionally, studies that contained an experimental trial with real patients and real surgeons were prioritized, although phantom and animal studies were also included. Major outcomes that were assessed included surgical error, anatomical localization accuracy, total procedural time, and patient outcome. RESULTS: A total of 37 studies were included. Overall, the implementation of CSTs had better surgical performance than the traditional methods. Most studies reported decreased error rate and increased efficiency. In terms of accuracy, most CSTs had over 90% accuracy in identifying anatomical markers with an error margin below 5 mm. Most studies reported a decrease in surgical time, although some were statistically insignificant. DISCUSSION: CSTs have been shown to reduce the mental workload of surgeons. However, the limited ergonomic design of current CSTs has hindered their widespread use in the clinical setting. Overall, more clinical data on actual patients is needed to provide concrete evidence before the ubiquitous implementation of CSTs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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 it