Assessment tool validation and technical skill improvement in the simulation of the Norwood operation using three-dimensional-printed heart models
Bibliographic record
Abstract
OBJECTIVES: The hands-on surgical training course utilizes 3-dimensional (3D)-printed heart models to simulate complex congenital heart operations. This study aimed to validate a model and assessment tool in the simulation of 2 techniques of the Norwood operation and investigate whether technical performance improves following rehearsal with or without proctor presence. METHODS: Five 'experienced' and 5 'junior' surgeons performed 2 techniques of the Norwood operation on 3D-printed models of hypoplastic left heart syndrome. Performances were retrospectively assessed by 10 raters with varying experience in congenital heart surgery (CHS) (Medical Doctorate versus non-Medical Doctorate). Assessments were made with the procedure-specific Hands-On Surgical Training-CHS tool. Results were analysed for technical performance and rater consistency. Following validation, 30 surgeons (24 with proctor guidance and 6 with training videos and objective feedback only) simulated the Norwood operation twice. RESULTS: Performance scores were consistently higher for experienced surgeons and raters discriminated clearly between the experienced and junior surgeons (P ≤ 0.001). The hands-on surgical training-CHS tool showed high inter-rater (0.86) and intra-rater (0.80) reliability among all raters. Scores for both experienced and junior surgeons were highly consistent across all raters, with no statistically significant difference (P = 0.50). All surgeons successfully performed the Norwood operation. Sixty attempts were scored in total. Eighty-seven percentage (26/30) of surgeons' scores (mean: attempt 1 = 92, attempt 2 = 104) and times [mean: attempt 1 = 1:22:00, attempt 2 = 1:08:00 (h:mm:ss)] improved between the 2 attempts by 9% and 15% respectively (P ≤ 0.001). Total scores of all surgeons in the non-proctored subgroup (6) improved by 15% on average (mean: attempt 1 = 86, attempt 2 = 105, P = 0.002). CONCLUSIONS: Procedure-specific assessment tools can be developed to evaluate technical performance for complex CHS simulation and be performed reliably by non-expert raters. Rehearsal both under supervision and independently leads to technical skill improvement further supporting its value in CHS training.
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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".