Comparison of Personal Video Technology for Teaching and Assessment of Surgical Skills
Bibliographic record
Abstract
BACKGROUND: Improvements in personal technology have made video recording for teaching and assessment of surgical skills possible. OBJECTIVE: This study compared 5 personal video-recording devices based on their utility (image quality, hardware, mounting options, and accessibility) in recording open surgical procedures. METHODS: Open procedures in a simulated setting were recorded using smartphones and tablets (MOB), laptops (LAP), sports cameras such as GoPro (SC), single-lens reflex cameras (DSLR), and spy camera glasses (SPY). Utility was rated by consensus between 2 investigators trained in observation of technology using a 5-point Likert scale (1, poor, to 5, excellent). RESULTS: A total of 150 hours of muted video were reviewed with a minimum 1 hour for each device. Image quality was good (3.8) across all devices, although this was influenced by the device-mounting requirements (4.2) and its proximity to the area of interest. Device hardware (battery life and storage capacity) was problematic for long procedures (3.8). Availability of devices was high (4.2). CONCLUSIONS: Personal video-recording technology can be used for assessment and teaching of open surgical skills. DSLR and SC provide the best images. DSLR provides the best zoom capability from an offset position, while SC can be placed closer to the operative field without impairing sterility. Laptops provide best overall utility for long procedures due to video file size. All devices require stable recording platforms (eg, bench space, dedicated mounting accessories). Head harnesses (SC, SPY) provide opportunities for "point-of-view" recordings. MOB and LAP can be used for multiple concurrent recordings.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".