Quality of laparoscopic camera navigation in robot‐assisted versus conventional laparoscopic surgery for rectal cancer: An analysis of surgical videos through a video processing computer software
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: To compare laparoscopic camera navigation (LCN) quality between robot-assisted laparoscopic surgery (RALS) and conventional laparoscopic surgery (CLS). METHODS: 20 recordings were selected by propensity score matching and subjected to Python® software to generate single frames at one second intervals. For each frame, the pixel where the camera should be centred, based on instrument position, current action (dissection/haemostasis/traction) in the frame, was detected. LCN quality was reviewed by two independent surgeons to evaluate erroneous LCN. RESULTS: RALS had higher incidence of centred views (83.1 ± 4.02% vs. 76.0 ± 2.38%, p < 0.05) and a shorter distance between actual and optimal frame centres (123.3 ± 9.8 vs. 144.8 ± 13.9, p < 0.05) compared to CLS. Erroneous camera navigations were more frequent in CLS regarding total time of horizontal alignment failure (2.1 ± 2.2 vs. 6.0 ± 5.4 min, p = 0.063) and number of excessive zoom-in visualization (0.1 ± 0.3 vs. 1.9 ± 1.4, p = 0.003). CONCLUSIONS: RALS provided higher LCN quality than did CLS, emphasising the benefits of a surgeon-controlled view.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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