The influence of surgical and procedural rotations and interest in a surgical discipline on medical students’ suturing ability during clerkship
Bibliographic record
Abstract
Background: Although suturing is an essential competency for medical students, there has been limited research into the skills acquisition process over the course of medical school curriculum. This study aimed to determine whether suturing ability improved over the course of clerkship and whether an interest in a surgical discipline was associated with improved skill acquisition. Methods: The suturing ability of third-year medical students at a large Canadian medical school was assessed at the beginning of clerkship (August 2018) as well as before and after their surgery rotation by 2 expert reviewers using a validated, objective scoring system as well as a qualitative assessment, both in person and via blinded video recordings. Students were randomly allocated to 4 groups for their clerkship year by the medical school. Results: Of 133 eligible students, 115 (86.5%) completed the study. Median suturing assessment scores improved significantly after the surgery rotation (214.5 [interquartile range (IQR) 191.1–235.0] v. 238.0 [IQR 223.5–255.0], p = 0.001). Groups that had completed a procedural rotation (emergency medicine, obstetrics and gynecology) between clerkship and starting their surgery rotation had improved scores between these time points (p < 0.05), whereas scores decreased for groups that did not have a procedural rotation between assessments. Regardless of previous rotations, suturing scores were similar between groups after the surgery rotation. The 21 students (18.3%) who were interested in a surgical discipline had higher suturing scores than students who were not interested in surgery at the beginning of clerkship (229.1 [IQR 220.2–253.0] v. 208.0 [IQR 185.0–228.0], p < 0.001) and after the surgery rotation (252.0 [IQR 227.0–268.0] v. 235.8 [IQR 220.5–251.2], p = 0.02). Conclusion: Medical students’ suturing ability improved during the surgery rotation but was also influenced by other procedural rotations and students’ interest in procedure specialties. Skill acquisition by medical students is complex and requires additional investigation.
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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".