In vitro evaluation of square and surgeon's knots in large gauge suture
Bibliographic record
Abstract
OBJECTIVE: To investigate the strength and size of surgeon's and square knots for starting and ending continuous suture lines using large gauge suture. STUDY DESIGN: In vitro mechanical study. STUDY POPULATION: Knotted suture. METHODS: Surgeon's and square knots were tested using 2 and 3 USP polyglactin 910 and 2 USP polydioxanone under linear tension on a universal testing machine. Failure mode and knot holding capacity (KHC) were recorded, and relative knot security (RKS) was calculated as a percentage of KHC. Comparisons were made between number of throws, suture size, suture type, and knot types. Knot volume and weight were assessed by a digital micrometer and balance, respectively. RESULTS: There were no significant differences in KHC (P = .295), RKS (P = .307), volume (P = .128), or weight (P = .310) between square and surgeon's knots at the start or end of suture lines with the same number of throws and suture type. A minimum of 6 throws were required for start knots and 7 throws at end knots to prevent unraveling. Knots tied with 3 polyglactin 910 were strongest (P < .001) and 2 polyglactin 910 produced knots with higher KHC and RKS than 2 polydioxanone (P < .001). CONCLUSION: No consistent differences were detected between knots types tied with the same suture material; however, number of throws affected KHC and RKS up to 6 throws in start or 7 throws in end knots. The configuration of square and surgeon's knots performed at the end of a continuous line alters their KHC, supporting the use of additional throws for knot security.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".