Force application of laparoscopic surgeons under the impact of heavy personal protective equipment during COVID-19 pandemic
Bibliographic record
Abstract
Surgeons are required to wear heavy personal protective equipment while delivering care to patients during the COVID-19 pandemic. We examined the impact of wearing double gloves on surgeons’ performance in laparoscopic surgery. Eleven surgeons-in-training at the Surgical Simulation Research Lab of the University of Alberta were recruited to perform laparoscopic cutting tasks in simulation while wearing none, one pair, and two pairs of surgical gloves. Forces applied to laparoscopic instruments were measured. Wearing gloves prolonged task times (one pair of gloves: 301.6 ± 61.7 s; two pairs of gloves: 295.8 ± 65.3 s) compared with no gloves (241.7 ± 46.9 s; p = 0.043). Wearing double gloves increased cutting errors (20.4 ± 5.1 mm2) compared with wearing one pair of gloves (16.9 ± 5.5 mm2) and no gloves (14.4 ± 4.6 mm2; p = 0.030). Wearing gloves reduced the peak force (one pair of gloves: 2.4 ± 0.7 N; two pairs of gloves: 2.7 ± 0.6 N; no gloves: 3.4 ± 1.4 N; p = 0.049), and the total force (one pair of gloves: 10.1 ± 2.8 N; two pairs of gloves: 10.3 ± 2.6 N; no glove: 12.6 ± 1.9 N; p = 0.048) delivered onto laparoscopic scissors compared with wearing no glove. The combined effects of wearing heavy gloves and using tools reduced the touching sensation, which limited the surgeons’ confidence in performing surgical tasks. Increasing practice in simulation is suggested to allow surgeons to overcome difficulties brought by personal protective equipment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".