Comparison of knot-tying proficiency and knot characteristics for square and reversing half hitch alternating-post surgical knots in a simulated deep body cavity among novice medical students
Bibliographic record
Abstract
Background: Proficiency-based knot-tying curricula have been developed for square knots for medical students, but, to our knowledge, no such curriculum exists for the reverse half hitch alternating-post (RHAP) knot. We aimed to compare medical students’ knot-tying proficiency, knot-tying self-confidence and final knot characteristics for RHAP and square knots in a simulated deep body cavity. Methods: We performed a within-subject prospective crossover study of novice medical students who received 30 minutes of training in tying both RHAP and square knots. Participant performance was assessed via a knot-tying checklist, and knot configuration, tensile strength, tightness (loop circumference) and mechanism of failure were also assessed. Participants’ self-reported confidence in knot tying was captured. Results: Twenty-one students participated in the study. Mean scores on the knot-tying checklist were significantly higher for RHAP knots than for square knots (6.9 [standard deviation (SD) 2.1] v. 5.2 [SD 2.3], p < 0.01), and RHAP knots were significantly tighter than square knots (46.8 mm [SD 0.4 mm] v. 49.3 mm [SD 0.7 mm], p < 0.05). There were no differences between RHAP and square knots in correct knot configuration, breaking strength or mechanism of failure. Reverse half hitch alternating-post knots were easier to tie within a deep-body cavity, whereas square knots were easier to learn. Conclusion: Novice medical students were more proficient in tying RHAP knots than square knots in a simulated deep body cavity. Students were able to construct RHAP knots more securely and reported increased confidence in tying RHAP knots at depth compared to square knots.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".