Quantifying lens obstructions in minimally invasive surgery: the impact on performance and outcomes
Bibliographic record
Abstract
Surgeons performing laparoscopic surgery depend primarily on their vision to operate, but it often gets obstructed by fog, smoke, and other debris. This mini-review examines the literature on lens obstruction, aiming to quantify its prevalence, identify factors affecting its frequency, evaluate its impacts on surgeons and patients, and present an overview of mitigation methods. The review reveals that there are typically between 3.5-15 lens obstruction events per procedure, and surgeons spend between 19% and 52% of the procedure with suboptimal vision. Additionally, 2% to 8% of the operating time is devoted to cleaning the scope. Factors influencing the frequency of lens obstructions include instrument selection, operating time, and surgeon experience. Lens obstructions may increase operating time, the risk of medical errors, and mental fatigue, though quantifiable results on this subject remain sparse. The review also highlights significant knowledge gaps in the field of lens obstructions during minimally invasive procedures and proposes several recommendations to accelerate research in this area.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".