Video Technologies for Recording Open Surgery: A Systematic Review
Bibliographic record
Abstract
Video recording of surgical procedures is an important tool for surgical education, performance enhancement, and error analysis. Technology for video recording open surgery, however, is limited. The objective of this article is to provide an overview of the available literature regarding the various technologies used for intraoperative video recording of open surgery. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines using the MEDLINE, Cochrane Central, and EMBASE databases. Two authors independently screened the titles and abstracts of the retrieved articles, and those that satisfied the defined inclusion criteria were selected for a full-text review. A total of 2275 publications were initially identified, and 110 were included in the final review. The included articles were categorized based on type of article, surgical subspecialty, type and positioning of camera, and limitations identified with their use. The most common article type was primary-technical (29%), and the dominant specialties were general surgery (22%) and plastic surgery (18%). The most commonly cited camera used was the GoPro (30%) positioned in a head-mount configuration (60%). Commonly cited limitations included poor video quality, inadequate battery life, light overexposure, obstruction by surgical team members, and excessive motion. Open surgery remains the mainstay of many surgical specialties today, and technological innovation is absolutely critical to fulfill the unmet need for better video capture of open surgery. The findings of this article will be valuable for guiding future development of novel technology for this purpose.
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.
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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".