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
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.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".