Proof of Concept Study: Investigating Force Metrics of an Intracorporeal Suturing Knot Task
Bibliographic record
Abstract
BACKGROUND: Mastering proper force manipulation in minimally invasive surgery can take many hours of practice and training. Improper force control can lead to necrosis, infection, and scarring. A force-sensing skin (FSS) has been developed, which measures forces at the distal end of minimal access surgeries' (MAS) instruments without altering the instrument's structural integrity or the surgical workflow, and acts as a minimally disruptive add-on to any MAS instrument. METHODS: A proof of concept study was conducted using a FSS-equipped 5 mm straight-tip needle holder. Participants (n = 19: 3 novices, 11 fellows, and 5 staff surgeons) performed one intracorporeal suturing knot task (ISKT). Using participant task video footage, each participant's two puncture forces (each wall of the Penrose drain) and three knot tightening forces were measured. Force metrics from the three expertise groups were compared using analysis of variance (ANOVA) and Tukey's honest significance test with statistical significance assessed at P < .05. RESULTS: Preliminary ISKT force metric data showed differences between novices and more experienced fellows and surgeons. Of the five stages of the ISKT evaluated, the first puncture force of the Penrose drain seemed to best reflect the difference in skill among participants. The study demonstrated ISKT knot tightening and puncture force ranges across three expertise levels (novices, surgical fellows, and staff surgeons) of 0.586 to 6.089 newtons (N) and 0.852 to 2.915 N, respectively. CONCLUSION: The investigation of force metrics is important for the implementation of future force feedback systems as it can provide real-time information to surgeons in training and the operating theater.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".