The Surgeon's View: Comparison of Two Digital Video Recording Systems in Veterinary Surgery
Bibliographic record
Abstract
Video recording and photography during surgical procedures are useful in veterinary medicine for several reasons, including legal, educational, and archival purposes. Many systems are available, such as hand cameras, light-mounted cameras, and head cameras. We chose a reasonably priced head camera that is among the smallest video cameras available. To best describe its possible uses and advantages, we recorded video and images of eight different surgical cases and procedures, both in hospital and field settings. All procedures were recorded both with a head-mounted camera and a commercial hand-held photo camera. Then sixteen volunteers (eight senior clinicians and eight final-year students) completed an evaluation questionnaire. Both cameras produced high-quality photographs and videos, but observers rated the head camera significantly better regarding point of view and their understanding of the surgical operation. The head camera was considered significantly more useful in teaching surgical procedures. Interestingly, senior clinicians tended to assign generally lower scores compared to students. The head camera we tested is an effective, easy-to-use tool for recording surgeries and various veterinary procedures in all situations, with no need for assistance from a dedicated operator. It can be a valuable aid for veterinarians working in all fields of the profession and a useful tool for veterinary surgical education.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".