Assessing and evaluating the impact of operative vision compromise (OViC) on surgeons’ practice: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Assessing the effects of compromised vision in laparoscopic and robotic procedures is crucial to understanding its impact on surgical practice and patient safety. Our aim was to examine the impact of operative vision compromise (OViC) on surgeons' practice. METHODS: Intraoperative workload was qualitatively assessed using the NASA-TLX score. Participants included internationally trained surgeons performing laparoscopic sleeve gastrectomy (LSG) procedures. Video recordings of LSG procedures were quantitatively analyzed to assess OViC event frequency and duration to determine their influence on procedural time and surgical flow in a secondary care center. Surgeons' views on OViC were assessed using a custom survey. Cost analysis of basic expenditures was performed. RESULTS: Among 109 participants, the overall NASA-TLX score for OViC was 71.7, indicating a high workload. Out of 81 LSG procedures, 77 experienced at least one lens fouling episode, resulting in 471 OViC events, including 371 lens cleaning occurrences. Significant positive correlations were found between total procedure time and several OViC variables. Compromised vision accounted for 19.3% of total operative time. Lens cleaning constituted 2.5% of the total operative time. In nine (11%) cases, lens cleaning added an average of 7 min per procedure, with the most severe case adding 15 min of operative time. The majority of surgeons (94%) found OViC to impair their performance and compromise patient safety, with 61% reporting witnessing surgical errors or complications directly attributable to OViC. CONCLUSIONS: OViC was linked to increased procedure time, surgical flow disruptions, elevated surgeon workload, cognitive burden, and frustration, and potential patient safety concerns. These findings emphasize the need for innovative solutions to mitigate OViC, thereby potentially minimizing errors and enhancing operative outcomes.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".